ShamirβTromer, 2003 Example: The IP address of Users seem LenstraβTromerβShamirβ dnssec-deployment.org 1. βThe rtsmitβDodsonβHughesβ is signed by an RSA-1024 key more than Leyland, 2005 Geiselmannβ signed by an RSA-2048 key 2. βThe ShamirβSteinwandtβTromer, 2005 signed by org βs RSA-1024 key off-the-shelf; eβKleinjungβPaarβPelzlβ signed by an RSA-2048 key attackers PriplataβStahlke, etc.: RSA-1024 signed by a root RSA-1024 key 3. For signatures: reakable in a year by an attack signed by an RSA-2048 key. switch keys machine costing β 10 9 dollars. Most βDNSSECβ signatures the attack Internet switched to follow a similar pattern. RSA-2048, and we no longer care Another example: SSL has used RSA-1024 security, right? many millions of RSA-1024 keys. rong! Imagine that an attacker has recorded tons of SSL traffic.
romer, 2003 Example: The IP address of Users seem unconcerned: romerβShamirβ dnssec-deployment.org 1. βThe attack machine dsonβHughesβ is signed by an RSA-1024 key more than this RSA Geiselmannβ signed by an RSA-2048 key 2. βThe attack machine andtβTromer, 2005 signed by org βs RSA-1024 key off-the-shelf; itβs only ungβPaarβPelzlβ signed by an RSA-2048 key attackers building e, etc.: RSA-1024 signed by a root RSA-1024 key 3. For signatures: year by an attack signed by an RSA-2048 key. switch keys every month, β 10 9 dollars. Most βDNSSECβ signatures the attack machine switched to follow a similar pattern. we no longer care Another example: SSL has used security, right? many millions of RSA-1024 keys. Imagine that an attacker has recorded tons of SSL traffic.
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Example: The IP address of Users seem unconcerned: dnssec-deployment.org 1. βThe attack machine costs is signed by an RSA-1024 key more than this RSA key is worth.β signed by an RSA-2048 key 2. βThe attack machine isnβt signed by org βs RSA-1024 key off-the-shelf; itβs only for signed by an RSA-2048 key attackers building ASICs.β signed by a root RSA-1024 key 3. For signatures: βWe signed by an RSA-2048 key. switch keys every month, and Most βDNSSECβ signatures the attack machine takes a year.β follow a similar pattern. Another example: SSL has used many millions of RSA-1024 keys. Imagine that an attacker has recorded tons of SSL traffic.
Example: The IP address of Users seem unconcerned: dnssec-deployment.org 1. βThe attack machine costs is signed by an RSA-1024 key more than this RSA key is worth.β signed by an RSA-2048 key 2. βThe attack machine isnβt signed by org βs RSA-1024 key off-the-shelf; itβs only for signed by an RSA-2048 key attackers building ASICs.β signed by a root RSA-1024 key 3. For signatures: βWe signed by an RSA-2048 key. switch keys every month, and Most βDNSSECβ signatures the attack machine takes a year.β follow a similar pattern. Real quote: βDNSSEC signing Another example: SSL has used keys should be large enough to many millions of RSA-1024 keys. avoid all known cryptographic Imagine that an attacker has attacks during the effectivity recorded tons of SSL traffic. period of the key.β
Example: The IP address of Users seem unconcerned: Continuation despite huge dnssec-deployment.org 1. βThe attack machine costs signed by an RSA-1024 key broken a more than this RSA key is worth.β by an RSA-2048 key fact, the 2. βThe attack machine isnβt by org βs RSA-1024 key estimated off-the-shelf; itβs only for by an RSA-2048 key of a 700-bit attackers building ASICs.β by a root RSA-1024 key breaking 3. For signatures: βWe by an RSA-2048 key. would need switch keys every month, and amounts βDNSSECβ signatures the attack machine takes a year.β power in a similar pattern. be detected Real quote: βDNSSEC signing Another example: SSL has used single key keys should be large enough to millions of RSA-1024 keys. estimated avoid all known cryptographic Imagine that an attacker has safely use attacks during the effectivity rded tons of SSL traffic. least the period of the key.β
address of Users seem unconcerned: Continuation of quote: despite huge efforts, dnssec-deployment.org 1. βThe attack machine costs RSA-1024 key broken a regular 1024-bit more than this RSA key is worth.β RSA-2048 key fact, the best completed 2. βThe attack machine isnβt RSA-1024 key estimated to be the off-the-shelf; itβs only for RSA-2048 key of a 700-bit key. An attackers building ASICs.β RSA-1024 key breaking a 1024-bit 3. For signatures: βWe RSA-2048 key. would need to exp switch keys every month, and amounts of network βDNSSECβ signatures the attack machine takes a year.β power in a way that pattern. be detected in order Real quote: βDNSSEC signing example: SSL has used single key. Because keys should be large enough to RSA-1024 keys. estimated that most avoid all known cryptographic attacker has safely use 1024-bit attacks during the effectivity SSL traffic. least the next ten period of the key.β
of Users seem unconcerned: Continuation of quote: βTo despite huge efforts, no one 1. βThe attack machine costs key broken a regular 1024-bit key; more than this RSA key is worth.β ey fact, the best completed attack 2. βThe attack machine isnβt key estimated to be the equivalent off-the-shelf; itβs only for ey of a 700-bit key. An attacker attackers building ASICs.β RSA-1024 key breaking a 1024-bit signing k 3. For signatures: βWe ey. would need to expend phenomenal switch keys every month, and amounts of networked computing signatures the attack machine takes a year.β power in a way that would not be detected in order to break Real quote: βDNSSEC signing as used single key. Because of this, it keys should be large enough to RSA-1024 keys. estimated that most zones c avoid all known cryptographic has safely use 1024-bit keys for at attacks during the effectivity traffic. least the next ten years.β period of the key.β
Users seem unconcerned: Continuation of quote: βTo date, despite huge efforts, no one has 1. βThe attack machine costs broken a regular 1024-bit key; in more than this RSA key is worth.β fact, the best completed attack is 2. βThe attack machine isnβt estimated to be the equivalent off-the-shelf; itβs only for of a 700-bit key. An attacker attackers building ASICs.β breaking a 1024-bit signing key 3. For signatures: βWe would need to expend phenomenal switch keys every month, and amounts of networked computing the attack machine takes a year.β power in a way that would not be detected in order to break a Real quote: βDNSSEC signing single key. Because of this, it is keys should be large enough to estimated that most zones can avoid all known cryptographic safely use 1024-bit keys for at attacks during the effectivity least the next ten years.β period of the key.β
seem unconcerned: Continuation of quote: βTo date, Goal of ou despite huge efforts, no one has analyze the βThe attack machine costs broken a regular 1024-bit key; in specifically than this RSA key is worth.β fact, the best completed attack is ratio , of βThe attack machine isnβt estimated to be the equivalent off-the-shelf; itβs only for βManyβ: of a 700-bit key. An attacker ers building ASICs.β βPrice-perfo breaking a 1024-bit signing key signatures: βWe area-time would need to expend phenomenal keys every month, and amounts of networked computing βRAMβ attack machine takes a year.β power in a way that would not bit integers be detected in order to break a quote: βDNSSEC signing accessing single key. Because of this, it is should be large enough to realistic; ββ estimated that most zones can all known cryptographic βAsymptoticβ: safely use 1024-bit keys for at attacks during the effectivity suppress least the next ten years.β of the key.β speedups
unconcerned: Continuation of quote: βTo date, Goal of our paper: despite huge efforts, no one has analyze the asymptotic machine costs broken a regular 1024-bit key; in specifically price-perfo RSA key is worth.β fact, the best completed attack is ratio , of breaking many machine isnβt estimated to be the equivalent only for βManyβ: e.g. millions. of a 700-bit key. An attacker ing ASICs.β βPrice-performance breaking a 1024-bit signing key signatures: βWe area-time product would need to expend phenomenal every month, and amounts of networked computing βRAMβ metric (adding machine takes a year.β power in a way that would not bit integers has same be detected in order to break a βDNSSEC signing accessing array of single key. Because of this, it is large enough to realistic; β ββ β metric estimated that most zones can cryptographic βAsymptoticβ: We safely use 1024-bit keys for at the effectivity suppress polynomial least the next ten years.β ey.β speedups are superp
Continuation of quote: βTo date, Goal of our paper: despite huge efforts, no one has analyze the asymptotic cost, costs broken a regular 1024-bit key; in specifically price-performance worth.β fact, the best completed attack is ratio , of breaking many RSA isnβt estimated to be the equivalent βManyβ: e.g. millions. of a 700-bit key. An attacker βPrice-performance ratioβ: breaking a 1024-bit signing key area-time product for chips. would need to expend phenomenal and amounts of networked computing βRAMβ metric (adding two a year.β power in a way that would not bit integers has same cost as accessing array of size 2 64 ) is be detected in order to break a signing single key. Because of this, it is ough to realistic; β ββ β metric is realistic. estimated that most zones can cryptographic βAsymptoticβ: We systematically safely use 1024-bit keys for at effectivity suppress polynomial factors. least the next ten years.β speedups are superpolynomial.
Continuation of quote: βTo date, Goal of our paper: despite huge efforts, no one has analyze the asymptotic cost, broken a regular 1024-bit key; in specifically price-performance fact, the best completed attack is ratio , of breaking many RSA keys. estimated to be the equivalent βManyβ: e.g. millions. of a 700-bit key. An attacker βPrice-performance ratioβ: breaking a 1024-bit signing key area-time product for chips. would need to expend phenomenal amounts of networked computing βRAMβ metric (adding two 64- power in a way that would not bit integers has same cost as accessing array of size 2 64 ) is not be detected in order to break a single key. Because of this, it is realistic; β ββ β metric is realistic. estimated that most zones can βAsymptoticβ: We systematically safely use 1024-bit keys for at suppress polynomial factors. Our least the next ten years.β speedups are superpolynomial.
Continuation of quote: βTo date, Goal of our paper: Best result time β² 1 βΏ 185632 despite huge efforts, no one has analyze the asymptotic cost, β² βΏ a regular 1024-bit key; in specifically price-performance using chip ββ is β² 1 βΏ the best completed attack is ratio , of breaking many RSA keys. estimated to be the equivalent βManyβ: e.g. millions. Our main 700-bit key. An attacker a batch of β² βΏ βPrice-performance ratioβ: reaking a 1024-bit signing key time β² 1 βΏ 022400 area-time product for chips. need to expend phenomenal β² βΏ using chip amounts of networked computing βRAMβ metric (adding two 64- β² βΏ ββ per k in a way that would not bit integers has same cost as This pap accessing array of size 2 64 ) is not detected in order to break a at β² β¦ (1) , key. Because of this, it is realistic; β ββ β metric is realistic. speedup estimated that most zones can βAsymptoticβ: We systematically Results a use 1024-bit keys for at suppress polynomial factors. Our guess from the next ten years.β speedups are superpolynomial.
quote: βTo date, Goal of our paper: Best result known time β² 1 βΏ 185632 efforts, no one has analyze the asymptotic cost, using chip area β² 0 βΏ 1024-bit key; in specifically price-performance ββ is β² 1 βΏ 976052 . completed attack is ratio , of breaking many RSA keys. the equivalent βManyβ: e.g. millions. Our main result fo An attacker a batch of β² 0 βΏ 5 keys: βPrice-performance ratioβ: 1024-bit signing key time β² 1 βΏ 022400 area-time product for chips. expend phenomenal using chip area β² 1 βΏ orked computing βRAMβ metric (adding two 64- ββ per key is β² 1 βΏ 704000 that would not bit integers has same cost as This paper also looks accessing array of size 2 64 ) is not rder to break a at β² β¦ (1) , analyzing Because of this, it is realistic; β ββ β metric is realistic. speedup from early-ab most zones can βAsymptoticβ: We systematically Results are not what 1024-bit keys for at suppress polynomial factors. Our guess from 1982 P ten years.β speedups are superpolynomial.
o date, Goal of our paper: Best result known for one key time β² 1 βΏ 185632 one has analyze the asymptotic cost, using chip area β² 0 βΏ 790420 ; key; in specifically price-performance ββ is β² 1 βΏ 976052 . attack is ratio , of breaking many RSA keys. equivalent βManyβ: e.g. millions. Our main result for attacker a batch of β² 0 βΏ 5 keys: βPrice-performance ratioβ: signing key time β² 1 βΏ 022400 area-time product for chips. phenomenal using chip area β² 1 βΏ 181600 ; computing βRAMβ metric (adding two 64- ββ per key is β² 1 βΏ 704000 . not bit integers has same cost as This paper also looks more closely accessing array of size 2 64 ) is not reak a at β² β¦ (1) , analyzing asymptotic this, it is realistic; β ββ β metric is realistic. speedup from early-abort ECM. can βAsymptoticβ: We systematically Results are not what one would r at suppress polynomial factors. Our guess from 1982 Pomerance. speedups are superpolynomial.
Goal of our paper: Best result known for one key time β² 1 βΏ 185632 analyze the asymptotic cost, using chip area β² 0 βΏ 790420 ; specifically price-performance ββ is β² 1 βΏ 976052 . ratio , of breaking many RSA keys. βManyβ: e.g. millions. Our main result for a batch of β² 0 βΏ 5 keys: βPrice-performance ratioβ: time β² 1 βΏ 022400 area-time product for chips. using chip area β² 1 βΏ 181600 ; βRAMβ metric (adding two 64- ββ per key is β² 1 βΏ 704000 . bit integers has same cost as This paper also looks more closely accessing array of size 2 64 ) is not at β² β¦ (1) , analyzing asymptotic realistic; β ββ β metric is realistic. speedup from early-abort ECM. βAsymptoticβ: We systematically Results are not what one would suppress polynomial factors. Our guess from 1982 Pomerance. speedups are superpolynomial.
of our paper: Best result known for one key Asymptotic time β² 1 βΏ 185632 analyze the asymptotic cost, 1. Attack using chip area β² 0 βΏ 790420 ; ecifically price-performance is reduced, ββ is β² 1 βΏ 976052 . of breaking many RSA keys. can targe βManyβ: e.g. millions. Our main result for 2. Prima a batch of β² 0 βΏ 5 keys: βPrice-performance ratioβ: memory time β² 1 βΏ 022400 ime product for chips. for off-the-shelf using chip area β² 1 βΏ 181600 ; βRAMβ metric (adding two 64- ββ per key is β² 1 βΏ 704000 . 3. Attack integers has same cost as (and can This paper also looks more closely accessing array of size 2 64 ) is not breaking at β² β¦ (1) , analyzing asymptotic realistic; β ββ β metric is realistic. speedup from early-abort ECM. βAsymptoticβ: We systematically Results are not what one would ress polynomial factors. Our guess from 1982 Pomerance. eedups are superpolynomial.
er: Best result known for one key Asymptotic consequ time β² 1 βΏ 185632 asymptotic cost, 1. Attack cost per using chip area β² 0 βΏ 790420 ; rice-performance is reduced, so attack ββ is β² 1 βΏ 976052 . reaking many RSA keys. can target lower-value millions. Our main result for 2. Primary bottleneck a batch of β² 0 βΏ 5 keys: rmance ratioβ: memory factorizationβw time β² 1 βΏ 022400 duct for chips. for off-the-shelf graphics using chip area β² 1 βΏ 181600 ; (adding two 64- ββ per key is β² 1 βΏ 704000 . 3. Attack time is reduced same cost as (and can be reduced This paper also looks more closely of size 2 64 ) is not breaking key rotation. at β² β¦ (1) , analyzing asymptotic ββ metric is realistic. speedup from early-abort ECM. e systematically Results are not what one would olynomial factors. Our guess from 1982 Pomerance. erpolynomial.
Best result known for one key Asymptotic consequences: time β² 1 βΏ 185632 cost, 1. Attack cost per key using chip area β² 0 βΏ 790420 ; rmance is reduced, so attacker ββ is β² 1 βΏ 976052 . RSA keys. can target lower-value keys. Our main result for 2. Primary bottleneck is low- a batch of β² 0 βΏ 5 keys: ratioβ: memory factorizationβwell suited time β² 1 βΏ 022400 chips. for off-the-shelf graphics cards. using chip area β² 1 βΏ 181600 ; o 64- ββ per key is β² 1 βΏ 704000 . 3. Attack time is reduced as (and can be reduced more), This paper also looks more closely ) is not breaking key rotation. at β² β¦ (1) , analyzing asymptotic ββ realistic. speedup from early-abort ECM. systematically Results are not what one would rs. Our guess from 1982 Pomerance. olynomial.
Best result known for one key Asymptotic consequences: time β² 1 βΏ 185632 1. Attack cost per key using chip area β² 0 βΏ 790420 ; is reduced, so attacker ββ is β² 1 βΏ 976052 . can target lower-value keys. Our main result for 2. Primary bottleneck is low- a batch of β² 0 βΏ 5 keys: memory factorizationβwell suited time β² 1 βΏ 022400 for off-the-shelf graphics cards. using chip area β² 1 βΏ 181600 ; ββ per key is β² 1 βΏ 704000 . 3. Attack time is reduced (and can be reduced more), This paper also looks more closely breaking key rotation. at β² β¦ (1) , analyzing asymptotic speedup from early-abort ECM. Results are not what one would guess from 1982 Pomerance.
Best result known for one key Asymptotic consequences: time β² 1 βΏ 185632 1. Attack cost per key using chip area β² 0 βΏ 790420 ; is reduced, so attacker ββ is β² 1 βΏ 976052 . can target lower-value keys. Our main result for 2. Primary bottleneck is low- a batch of β² 0 βΏ 5 keys: memory factorizationβwell suited time β² 1 βΏ 022400 for off-the-shelf graphics cards. using chip area β² 1 βΏ 181600 ; ββ per key is β² 1 βΏ 704000 . 3. Attack time is reduced (and can be reduced more), This paper also looks more closely breaking key rotation. at β² β¦ (1) , analyzing asymptotic βDo the asymptotics really kick in speedup from early-abort ECM. before 1024 bits?β β Maybe not, Results are not what one would but no basis for confidence. guess from 1982 Pomerance.
result known for one key Asymptotic consequences: Eratosthenes β² 1 βΏ 185632 1. Attack cost per key Sieving small β β chip area β² 0 βΏ 790420 ; is reduced, so attacker using prim β β β β² 1 βΏ 976052 . ββ can target lower-value keys. 1 2 2 main result for 3 3 2. Primary bottleneck is low- batch of β² 0 βΏ 5 keys: 4 2 2 5 memory factorizationβwell suited 6 2 3 β² 1 βΏ 022400 7 for off-the-shelf graphics cards. 8 2 2 2 chip area β² 1 βΏ 181600 ; 9 3 3 10 2 er key is β² 1 βΏ 704000 . 3. Attack time is reduced ββ 11 12 2 2 3 (and can be reduced more), 13 paper also looks more closely 14 2 breaking key rotation. 15 3 β² β¦ (1) , analyzing asymptotic 16 2 2 2 2 17 βDo the asymptotics really kick in eedup from early-abort ECM. 18 2 3 3 19 before 1024 bits?β β Maybe not, Results are not what one would 20 2 2 but no basis for confidence. from 1982 Pomerance. etc.
wn for one key Asymptotic consequences: Eratosthenes for smo β² βΏ 1. Attack cost per key Sieving small integers β β β² 0 βΏ 790420 ; is reduced, so attacker using primes 2 β 3 β 5 β β² βΏ ββ can target lower-value keys. 1 2 2 for 3 3 2. Primary bottleneck is low- β² βΏ keys: 4 2 2 5 5 memory factorizationβwell suited 6 2 3 β² βΏ 7 7 for off-the-shelf graphics cards. 8 2 2 2 β² 1 βΏ 181600 ; 9 3 3 10 2 5 β² βΏ 704000 . 3. Attack time is reduced ββ 11 12 2 2 3 (and can be reduced more), 13 looks more closely 14 2 7 breaking key rotation. 15 3 5 β² β¦ analyzing asymptotic 16 2 2 2 2 17 βDo the asymptotics really kick in rly-abort ECM. 18 2 3 3 19 before 1024 bits?β β Maybe not, what one would 20 2 2 5 but no basis for confidence. Pomerance. etc.
key Asymptotic consequences: Eratosthenes for smoothness β² βΏ 1. Attack cost per key Sieving small integers β β 0 β² βΏ is reduced, so attacker using primes 2 β 3 β 5 β 7: β² βΏ ββ can target lower-value keys. 1 2 2 3 3 2. Primary bottleneck is low- 4 2 2 β² βΏ 5 5 memory factorizationβwell suited 6 2 3 β² βΏ 7 7 for off-the-shelf graphics cards. 8 2 2 2 β² βΏ 9 3 3 10 2 5 β² βΏ 3. Attack time is reduced ββ 11 12 2 2 3 (and can be reduced more), 13 re closely 14 2 7 breaking key rotation. 15 3 5 β² β¦ ptotic 16 2 2 2 2 17 βDo the asymptotics really kick in ECM. 18 2 3 3 19 before 1024 bits?β β Maybe not, would 20 2 2 5 but no basis for confidence. omerance. etc.
Asymptotic consequences: Eratosthenes for smoothness 1. Attack cost per key Sieving small integers β β 0 is reduced, so attacker using primes 2 β 3 β 5 β 7: can target lower-value keys. 1 2 2 3 3 2. Primary bottleneck is low- 4 2 2 5 5 memory factorizationβwell suited 6 2 3 7 7 for off-the-shelf graphics cards. 8 2 2 2 9 3 3 10 2 5 3. Attack time is reduced 11 12 2 2 3 (and can be reduced more), 13 14 2 7 breaking key rotation. 15 3 5 16 2 2 2 2 17 βDo the asymptotics really kick in 18 2 3 3 19 before 1024 bits?β β Maybe not, 20 2 2 5 but no basis for confidence. etc.
Asymptotic consequences: Eratosthenes for smoothness The Q sieve ttack cost per key Sieving small integers β β 0 Sieving β β β reduced, so attacker using primes 2 β 3 β 5 β 7: using prime β β β rget lower-value keys. 1 1 2 2 2 2 3 3 3 3 Primary bottleneck is low- 4 2 2 4 2 2 5 5 5 ry factorizationβwell suited 6 2 3 6 2 3 7 7 7 -the-shelf graphics cards. 8 2 2 2 8 2 2 2 9 3 3 9 3 3 10 2 5 10 2 ttack time is reduced 11 11 12 2 2 3 12 2 2 3 can be reduced more), 13 13 14 2 7 14 2 reaking key rotation. 15 3 5 15 3 16 2 2 2 2 16 2 2 2 2 17 17 the asymptotics really kick in 18 2 3 3 18 2 3 3 19 19 1024 bits?β β Maybe not, 20 2 2 5 20 2 2 basis for confidence. etc. etc.
consequences: Eratosthenes for smoothness The Q sieve er key Sieving small integers β β 0 Sieving β and 611 + β β attacker using primes 2 β 3 β 5 β 7: using primes 2 β 3 β 5 β er-value keys. 1 1 612 2 2 2 2 2 613 3 3 3 3 614 2 ottleneck is low- 4 2 2 4 2 2 615 5 5 5 5 616 2 rizationβwell suited 6 2 3 6 2 3 617 7 7 7 7 618 2 graphics cards. 8 2 2 2 8 2 2 2 619 9 3 3 9 3 3 620 2 10 2 5 10 2 5 621 is reduced 11 11 622 2 12 2 2 3 12 2 2 3 623 reduced more), 13 13 624 2 14 2 7 14 2 7 625 rotation. 15 3 5 15 3 5 626 2 16 2 2 2 2 16 2 2 2 2 627 17 17 628 2 asymptotics really kick in 18 2 3 3 18 2 3 3 629 19 19 630 2 bits?β β Maybe not, 20 2 2 5 20 2 2 5 631 confidence. etc. etc.
Eratosthenes for smoothness The Q sieve Sieving small integers β β 0 Sieving β and 611 + β for sm β using primes 2 β 3 β 5 β 7: using primes 2 β 3 β 5 β 7: eys. 1 1 612 2 2 3 3 2 2 2 2 613 3 3 3 3 614 2 low- 4 2 2 4 2 2 615 3 5 5 5 5 5 616 2 2 2 ell suited 6 2 3 6 2 3 617 7 7 7 7 618 2 3 cards. 8 2 2 2 8 2 2 2 619 9 3 3 9 3 3 620 2 2 5 10 2 5 10 2 5 621 3 3 3 11 11 622 2 12 2 2 3 12 2 2 3 623 re), 13 13 624 2 2 2 2 3 14 2 7 14 2 7 625 5 15 3 5 15 3 5 626 2 16 2 2 2 2 16 2 2 2 2 627 3 17 17 628 2 2 kick in 18 2 3 3 18 2 3 3 629 19 19 630 2 3 3 5 ybe not, 20 2 2 5 20 2 2 5 631 confidence. etc. etc.
Eratosthenes for smoothness The Q sieve Sieving small integers β β 0 Sieving β and 611 + β for small β using primes 2 β 3 β 5 β 7: using primes 2 β 3 β 5 β 7: 1 1 612 2 2 3 3 2 2 2 2 613 3 3 3 3 614 2 4 2 2 4 2 2 615 3 5 5 5 5 5 616 2 2 2 7 6 2 3 6 2 3 617 7 7 7 7 618 2 3 8 2 2 2 8 2 2 2 619 9 3 3 9 3 3 620 2 2 5 10 2 5 10 2 5 621 3 3 3 11 11 622 2 12 2 2 3 12 2 2 3 623 7 13 13 624 2 2 2 2 3 14 2 7 14 2 7 625 5 5 5 5 15 3 5 15 3 5 626 2 16 2 2 2 2 16 2 2 2 2 627 3 17 17 628 2 2 18 2 3 3 18 2 3 3 629 19 19 630 2 3 3 5 7 20 2 2 5 20 2 2 5 631 etc. etc.
Eratosthenes for smoothness The Q sieve Have complet the congruences β β β Sieving small integers β β 0 Sieving β and 611 + β for small β for some β primes 2 β 3 β 5 β 7: using primes 2 β 3 β 5 β 7: 14 β 625 1 612 2 2 3 3 2 2 613 64 β 675 3 3 3 614 2 4 2 2 615 3 5 75 β 686 5 5 5 616 2 2 2 7 3 6 2 3 617 7 7 7 618 2 3 14 β 64 β 75 β β β 8 2 2 2 619 3 3 9 3 3 620 2 2 5 = 2 8 3 4 5 8 5 10 2 5 621 3 3 3 11 622 2 3 12 2 2 3 623 7 β β gcd 611 β β β οΏ½ 13 624 2 2 2 2 3 7 14 2 7 625 5 5 5 5 = 47. 3 5 15 3 5 626 2 16 2 2 2 2 627 3 17 628 2 2 611 = 47 β 3 3 18 2 3 3 629 19 630 2 3 3 5 7 5 20 2 2 5 631 etc.
smoothness The Q sieve Have complete facto the congruences β β β integers β β 0 Sieving β and 611 + β for small β for some β βs. β β 5 β 7: using primes 2 β 3 β 5 β 7: 14 β 625 = 2 1 3 0 5 4 7 1 612 2 2 3 3 2 2 613 64 β 675 = 2 6 3 3 5 2 7 3 3 614 2 4 2 2 615 3 5 75 β 686 = 2 1 3 1 5 2 7 5 5 616 2 2 2 7 6 2 3 617 7 7 618 2 3 14 β 64 β 75 β 625 β 675 β 8 2 2 2 619 = 2 8 3 4 5 8 7 4 = (2 4 3 9 3 3 620 2 2 5 10 2 5 621 3 3 3 11 622 2 12 2 2 3 623 7 β β gcd 611 β 14 β 64 β 75 οΏ½ 13 624 2 2 2 2 3 14 2 7 625 5 5 5 5 = 47. 15 3 5 626 2 16 2 2 2 2 627 3 17 628 2 2 611 = 47 β 13. 18 2 3 3 629 19 630 2 3 3 5 7 20 2 2 5 631 etc.
othness The Q sieve Have complete factorization the congruences β β 611 + β β β 0 Sieving β and 611 + β for small β for some β βs. β β β using primes 2 β 3 β 5 β 7: 14 β 625 = 2 1 3 0 5 4 7 1 . 1 612 2 2 3 3 2 2 613 64 β 675 = 2 6 3 3 5 2 7 0 . 3 3 614 2 4 2 2 615 3 5 75 β 686 = 2 1 3 1 5 2 7 3 . 5 5 616 2 2 2 7 6 2 3 617 7 7 618 2 3 14 β 64 β 75 β 625 β 675 β 686 8 2 2 2 619 = 2 8 3 4 5 8 7 4 = (2 4 3 2 5 4 7 2 ) 2 . 9 3 3 620 2 2 5 10 2 5 621 3 3 3 11 622 2 611 β 14 β 64 β 75 οΏ½ 2 4 3 2 5 12 2 2 3 623 7 β β gcd 13 624 2 2 2 2 3 14 2 7 625 5 5 5 5 = 47. 15 3 5 626 2 16 2 2 2 2 627 3 17 628 2 2 611 = 47 β 13. 18 2 3 3 629 19 630 2 3 3 5 7 20 2 2 5 631 etc.
The Q sieve Have complete factorization of the congruences β β 611 + β Sieving β and 611 + β for small β for some β βs. using primes 2 β 3 β 5 β 7: 14 β 625 = 2 1 3 0 5 4 7 1 . 1 612 2 2 3 3 2 2 613 64 β 675 = 2 6 3 3 5 2 7 0 . 3 3 614 2 4 2 2 615 3 5 75 β 686 = 2 1 3 1 5 2 7 3 . 5 5 616 2 2 2 7 6 2 3 617 7 7 618 2 3 14 β 64 β 75 β 625 β 675 β 686 8 2 2 2 619 = 2 8 3 4 5 8 7 4 = (2 4 3 2 5 4 7 2 ) 2 . 9 3 3 620 2 2 5 10 2 5 621 3 3 3 11 622 2 611 β 14 β 64 β 75 οΏ½ 2 4 3 2 5 4 7 2 β 12 2 2 3 623 7 β gcd 13 624 2 2 2 2 3 14 2 7 625 5 5 5 5 = 47. 15 3 5 626 2 16 2 2 2 2 627 3 17 628 2 2 611 = 47 β 13. 18 2 3 3 629 19 630 2 3 3 5 7 20 2 2 5 631 etc.
sieve Have complete factorization of The numb the congruences β β 611 + β Sieving β and 611 + β for small β Generalize β β β β β for some β βs. primes 2 β 3 β 5 β 7: β¦ β β β ββ β 14 β 625 = 2 1 3 0 5 4 7 1 . β¦ β οΏ½ ββ β β οΏ½ ββ β οΏ½ β 612 2 2 3 3 613 64 β 675 = 2 6 3 3 5 2 7 0 . for root β β· 3 614 2 615 3 5 75 β 686 = 2 1 3 1 5 2 7 3 . of nonzero 5 616 2 2 2 7 3 617 7 618 2 3 14 β 64 β 75 β 625 β 675 β 686 For any β β 619 = 2 8 3 4 5 8 7 4 = (2 4 3 2 5 4 7 2 ) 2 . 3 3 620 2 2 5 so that facto β οΏ½ β 5 621 3 3 3 622 2 produces β 611 β 14 β 64 β 75 οΏ½ 2 4 3 2 5 4 7 2 β 3 623 7 β gcd 624 2 2 2 2 3 7 625 5 5 5 5 = 47. Optimal β 3 5 626 2 β β 627 3 ( β + β¦ (1))(log β β 628 2 2 611 = 47 β 13. 3 3 629 630 2 3 3 5 7 5 631
Have complete factorization of The number-field sieve the congruences β β 611 + β β 611 + β for small β Generalize β β β + β β for some β βs. β β 5 β 7: β¦ β β β + ββ (mo β 14 β 625 = 2 1 3 0 5 4 7 1 . β¦ β οΏ½ ββ β β οΏ½ ββ β οΏ½ β 2 2 3 3 64 β 675 = 2 6 3 3 5 2 7 0 . for root β β· C 2 3 5 75 β 686 = 2 1 3 1 5 2 7 3 . of nonzero integer 2 2 2 7 2 3 14 β 64 β 75 β 625 β 675 β 686 For any β can find β = 2 8 3 4 5 8 7 4 = (2 4 3 2 5 4 7 2 ) 2 . 2 2 5 so that factoring β οΏ½ β 3 3 3 2 produces factorization β 611 β 14 β 64 β 75 οΏ½ 2 4 3 2 5 4 7 2 β 7 β gcd 2 2 2 2 3 5 5 5 5 = 47. Optimal choice of β 2 ( β + β¦ (1))(log β ) 2 β β 3 β 2 2 611 = 47 β 13. 2 3 3 5 7
Have complete factorization of The number-field sieve the congruences β β 611 + β β β small β Generalize β β β + β (mod β for some β βs. β β β β¦ β β β + ββ (mod β ) 14 β 625 = 2 1 3 0 5 4 7 1 . β¦ β οΏ½ ββ β β οΏ½ ββ (mod β οΏ½ β 64 β 675 = 2 6 3 3 5 2 7 0 . for root β β· C 5 75 β 686 = 2 1 3 1 5 2 7 3 . of nonzero integer poly. 7 14 β 64 β 75 β 625 β 675 β 686 For any β can find β = 2 8 3 4 5 8 7 4 = (2 4 3 2 5 4 7 2 ) 2 . 5 so that factoring β οΏ½ β produces factorization of β . 611 β 14 β 64 β 75 οΏ½ 2 4 3 2 5 4 7 2 β 7 β gcd 5 5 5 5 = 47. Optimal choice of log β is ( β + β¦ (1))(log β ) 2 β 3 (log log β β 611 = 47 β 13. 5 7
Have complete factorization of The number-field sieve the congruences β β 611 + β Generalize β β β + β (mod β ) for some β βs. β¦ β β β + ββ (mod β ) 14 β 625 = 2 1 3 0 5 4 7 1 . β¦ β οΏ½ ββ β β οΏ½ ββ (mod β οΏ½ β ) 64 β 675 = 2 6 3 3 5 2 7 0 . for root β β· C 75 β 686 = 2 1 3 1 5 2 7 3 . of nonzero integer poly. 14 β 64 β 75 β 625 β 675 β 686 For any β can find β = 2 8 3 4 5 8 7 4 = (2 4 3 2 5 4 7 2 ) 2 . so that factoring β οΏ½ β produces factorization of β . 611 β 14 β 64 β 75 οΏ½ 2 4 3 2 5 4 7 2 β β gcd = 47. Optimal choice of log β is ( β + β¦ (1))(log β ) 2 β 3 (log log β ) 1 β 3 . 611 = 47 β 13.
complete factorization of The number-field sieve RAM cost congruences β β 611 + β Generalize β β β + β (mod β ) 1993 BuhlerβLenstraβP ome β βs. β² βΏ β¦ β β β + ββ (mod β ) Smoothness Sieve β² 1 βΏ β 625 = 2 1 3 0 5 4 7 1 . β¦ β οΏ½ ββ β β οΏ½ ββ (mod β οΏ½ β ) ββ β β 675 = 2 6 3 3 5 2 7 0 . Find β² 0 βΏ 961500 for root β β· C β 686 = 2 1 3 1 5 2 7 3 . of nonzero integer poly. with β οΏ½ ββ β οΏ½ ββ β² βΏ Total RAM β 75 β 625 β 675 β 686 For any β can find β β 5 8 7 4 = (2 4 3 2 5 4 7 2 ) 2 . so that factoring β οΏ½ β 1993 Copp β² βΏ produces factorization of β . Total RAM 611 β 14 β 64 β 75 οΏ½ 2 4 3 2 5 4 7 2 β β using multiple Optimal choice of log β is ( β + β¦ (1))(log β ) 2 β 3 (log log β ) 1 β 3 . (Multiple 47 β 13. donβt seem with ββ
factorization of The number-field sieve RAM cost analysis β β 611 + β Generalize β β β + β (mod β ) 1993 BuhlerβLenstraβP β Smoothness bound β² βΏ β¦ β β β + ββ (mod β ) Sieve β² 1 βΏ 923000 pairs ββ β 4 7 1 . β¦ β οΏ½ ββ β β οΏ½ ββ (mod β οΏ½ β ) β Find β² 0 βΏ 961500 pairs 2 7 0 . for root β β· C β 2 7 3 . of nonzero integer poly. with β οΏ½ ββ and β οΏ½ ββ β Total RAM time β² βΏ β 675 β 686 For any β can find β β β β (2 4 3 2 5 4 7 2 ) 2 . so that factoring β οΏ½ β 1993 Coppersmith: Total RAM time β² βΏ produces factorization of β . β 75 οΏ½ 2 4 3 2 5 4 7 2 β β β β using multiple numb Optimal choice of log β is ( β + β¦ (1))(log β ) 2 β 3 (log log β ) 1 β 3 . (Multiple number β donβt seem to combine with ββ , factory, et
rization of The number-field sieve RAM cost analysis β β β Generalize β β β + β (mod β ) 1993 BuhlerβLenstraβPomera β Smoothness bound β² 0 βΏ 961500 β¦ β β β + ββ (mod β ) Sieve β² 1 βΏ 923000 pairs ( ββ β ). β¦ β οΏ½ ββ β β οΏ½ ββ (mod β οΏ½ β ) β Find β² 0 βΏ 961500 pairs for root β β· C β of nonzero integer poly. with β οΏ½ ββ and β οΏ½ ββ smo β Total RAM time β² 1 βΏ 923000 . For any β can find β β β β β β . so that factoring β οΏ½ β 1993 Coppersmith: Total RAM time β² 1 βΏ 901884 produces factorization of β . 2 5 4 7 2 β β β β β οΏ½ using multiple number fields. Optimal choice of log β is ( β + β¦ (1))(log β ) 2 β 3 (log log β ) 1 β 3 . (Multiple number fields β donβt seem to combine well with ββ , factory, et al.)
The number-field sieve RAM cost analysis Generalize β β β + β (mod β ) 1993 BuhlerβLenstraβPomerance: Smoothness bound β² 0 βΏ 961500 . β¦ β β β + ββ (mod β ) Sieve β² 1 βΏ 923000 pairs ( ββ β ). β¦ β οΏ½ ββ β β οΏ½ ββ (mod β οΏ½ β ) Find β² 0 βΏ 961500 pairs for root β β· C of nonzero integer poly. with β οΏ½ ββ and β οΏ½ ββ smooth. Total RAM time β² 1 βΏ 923000 . For any β can find β so that factoring β οΏ½ β 1993 Coppersmith: Total RAM time β² 1 βΏ 901884 produces factorization of β . using multiple number fields. Optimal choice of log β is ( β + β¦ (1))(log β ) 2 β 3 (log log β ) 1 β 3 . (Multiple number fields donβt seem to combine well with ββ , factory, et al.)
number-field sieve RAM cost analysis ββ cost Generalize β β β + β (mod β ) 1993 BuhlerβLenstraβPomerance: Sieving is Smoothness bound β² 0 βΏ 961500 . β¦ β β β + ββ (mod β ) in realistic Sieve β² 1 βΏ 923000 pairs ( ββ β ). ββ cost β² βΏ β¦ β οΏ½ ββ β β οΏ½ ββ (mod β οΏ½ β ) Find β² 0 βΏ 961500 pairs ot β β· C nonzero integer poly. with β οΏ½ ββ and β οΏ½ ββ smooth. Total RAM time β² 1 βΏ 923000 . any β can find β that factoring β οΏ½ β 1993 Coppersmith: Total RAM time β² 1 βΏ 901884 duces factorization of β . using multiple number fields. Optimal choice of log β is β¦ (1))(log β ) 2 β 3 (log log β ) 1 β 3 . β (Multiple number fields donβt seem to combine well with ββ , factory, et al.)
er-field sieve RAM cost analysis ββ cost analysis β β β + β (mod β ) 1993 BuhlerβLenstraβPomerance: Sieving is a disaster Smoothness bound β² 0 βΏ 961500 . β¦ β β β ββ (mod β ) in realistic cost metric. Sieve β² 1 βΏ 923000 pairs ( ββ β ). ββ cost β² 2 βΏ 403750 . β¦ β οΏ½ ββ β β οΏ½ ββ (mod β οΏ½ β ) Find β² 0 βΏ 961500 pairs β β· integer poly. with β οΏ½ ββ and β οΏ½ ββ smooth. Total RAM time β² 1 βΏ 923000 . β find β β οΏ½ β 1993 Coppersmith: Total RAM time β² 1 βΏ 901884 zation of β . using multiple number fields. of log β is β ) 2 β 3 (log log β ) 1 β 3 . β β¦ (Multiple number fields donβt seem to combine well with ββ , factory, et al.)
RAM cost analysis ββ cost analysis β β β β (mod β ) 1993 BuhlerβLenstraβPomerance: Sieving is a disaster Smoothness bound β² 0 βΏ 961500 . β¦ β β β ββ β in realistic cost metric. Sieve β² 1 βΏ 923000 pairs ( ββ β ). ββ cost β² 2 βΏ 403750 . β¦ β οΏ½ ββ β β οΏ½ ββ β οΏ½ β ) Find β² 0 βΏ 961500 pairs β β· with β οΏ½ ββ and β οΏ½ ββ smooth. Total RAM time β² 1 βΏ 923000 . β β β οΏ½ β 1993 Coppersmith: Total RAM time β² 1 βΏ 901884 β . using multiple number fields. β β log β ) 1 β 3 . β β¦ β (Multiple number fields donβt seem to combine well with ββ , factory, et al.)
RAM cost analysis ββ cost analysis 1993 BuhlerβLenstraβPomerance: Sieving is a disaster Smoothness bound β² 0 βΏ 961500 . in realistic cost metric. Sieve β² 1 βΏ 923000 pairs ( ββ β ). ββ cost β² 2 βΏ 403750 . Find β² 0 βΏ 961500 pairs with β οΏ½ ββ and β οΏ½ ββ smooth. Total RAM time β² 1 βΏ 923000 . 1993 Coppersmith: Total RAM time β² 1 βΏ 901884 using multiple number fields. (Multiple number fields donβt seem to combine well with ββ , factory, et al.)
RAM cost analysis ββ cost analysis 1993 BuhlerβLenstraβPomerance: Sieving is a disaster Smoothness bound β² 0 βΏ 961500 . in realistic cost metric. Sieve β² 1 βΏ 923000 pairs ( ββ β ). ββ cost β² 2 βΏ 403750 . Find β² 0 βΏ 961500 pairs Fix: find smooth using ECM. with β οΏ½ ββ and β οΏ½ ββ smooth. ββ cost β² 1 βΏ 923000 . Total RAM time β² 1 βΏ 923000 . 1993 Coppersmith: Total RAM time β² 1 βΏ 901884 using multiple number fields. (Multiple number fields donβt seem to combine well with ββ , factory, et al.)
RAM cost analysis ββ cost analysis 1993 BuhlerβLenstraβPomerance: Sieving is a disaster Smoothness bound β² 0 βΏ 961500 . in realistic cost metric. Sieve β² 1 βΏ 923000 pairs ( ββ β ). ββ cost β² 2 βΏ 403750 . Find β² 0 βΏ 961500 pairs Fix: find smooth using ECM. with β οΏ½ ββ and β οΏ½ ββ smooth. ββ cost β² 1 βΏ 923000 . Total RAM time β² 1 βΏ 923000 . Linear algebra is also a disaster. 1993 Coppersmith: ββ cost β² 2 βΏ 403750 . Total RAM time β² 1 βΏ 901884 using multiple number fields. (Multiple number fields donβt seem to combine well with ββ , factory, et al.)
RAM cost analysis ββ cost analysis 1993 BuhlerβLenstraβPomerance: Sieving is a disaster Smoothness bound β² 0 βΏ 961500 . in realistic cost metric. Sieve β² 1 βΏ 923000 pairs ( ββ β ). ββ cost β² 2 βΏ 403750 . Find β² 0 βΏ 961500 pairs Fix: find smooth using ECM. with β οΏ½ ββ and β οΏ½ ββ smooth. ββ cost β² 1 βΏ 923000 . Total RAM time β² 1 βΏ 923000 . Linear algebra is also a disaster. 1993 Coppersmith: ββ cost β² 2 βΏ 403750 . Total RAM time β² 1 βΏ 901884 Semi-fix: Reduce smoothness using multiple number fields. bounds to rebalance. (Multiple number fields ββ cost β² 1 βΏ 976052 . donβt seem to combine well (2001 Bernstein) with ββ , factory, et al.)
cost analysis ββ cost analysis The facto BuhlerβLenstraβPomerance: Sieving is a disaster 1993 Copp othness bound β² 0 βΏ 961500 . in realistic cost metric. There exists β² 1 βΏ 923000 pairs ( ββ β ). ββ cost β² 2 βΏ 403750 . that facto β² 0 βΏ 961500 pairs with same β Fix: find smooth using ECM. β² βΏ β οΏ½ ββ and β οΏ½ ββ smooth. in RAM ββ cost β² 1 βΏ 923000 . RAM time β² 1 βΏ 923000 . β² βΏ Smoothness Linear algebra is also a disaster. Coppersmith: Smaller than ββ cost β² 2 βΏ 403750 . RAM time β² 1 βΏ 901884 so need mo ββ β Semi-fix: Reduce smoothness multiple number fields. Algorithm ββ β bounds to rebalance. (Multiple number fields such that β οΏ½ ββ ββ cost β² 1 βΏ 976052 . seem to combine well Note: one β β (2001 Bernstein) ββ , factory, et al.) Algorithm whether β οΏ½ ββ β
analysis ββ cost analysis The factorization facto BuhlerβLenstraβPomerance: Sieving is a disaster 1993 Coppersmith: ound β² 0 βΏ 961500 . in realistic cost metric. There exists an algo β² βΏ ββ cost β² 2 βΏ 403750 . pairs ( ββ β ). that factors any integer β² βΏ pairs with same #bits as β Fix: find smooth using ECM. in RAM time β² 1 βΏ 638587 β οΏ½ ββ and β οΏ½ ββ smooth. ββ cost β² 1 βΏ 923000 . β² 1 βΏ 923000 . Smoothness bound β² βΏ Linear algebra is also a disaster. ersmith: Smaller than before, ββ cost β² 2 βΏ 403750 . β² 1 βΏ 901884 so need more ( ββ β Semi-fix: Reduce smoothness number fields. Algorithm knows all ββ β bounds to rebalance. er fields such that β οΏ½ ββ ββ cost β² 1 βΏ 976052 . combine well Note: one β works β (2001 Bernstein) ββ , et al.) Algorithm uses ECM whether β οΏ½ ββ β is
ββ cost analysis The factorization factory omerance: Sieving is a disaster 1993 Coppersmith: β² βΏ 961500 . in realistic cost metric. There exists an algorithm β² βΏ ββ cost β² 2 βΏ 403750 . ββ β ). that factors any integer β² βΏ with same #bits as β Fix: find smooth using ECM. in RAM time β² 1 βΏ 638587 . β οΏ½ ββ β οΏ½ ββ smooth. ββ cost β² 1 βΏ 923000 . β² βΏ . Smoothness bound β² 0 βΏ 819290 Linear algebra is also a disaster. Smaller than before, ββ cost β² 2 βΏ 403750 . β² βΏ so need more ( ββ β ). Semi-fix: Reduce smoothness fields. Algorithm knows all ( ββ β ) bounds to rebalance. such that β οΏ½ ββ is smooth. ββ cost β² 1 βΏ 976052 . ell Note: one β works for all β (2001 Bernstein) ββ Algorithm uses ECM to check whether β οΏ½ ββ β is smooth.
ββ cost analysis The factorization factory Sieving is a disaster 1993 Coppersmith: in realistic cost metric. There exists an algorithm ββ cost β² 2 βΏ 403750 . that factors any integer with same #bits as β Fix: find smooth using ECM. in RAM time β² 1 βΏ 638587 . ββ cost β² 1 βΏ 923000 . Smoothness bound β² 0 βΏ 819290 . Linear algebra is also a disaster. Smaller than before, ββ cost β² 2 βΏ 403750 . so need more ( ββ β ). Semi-fix: Reduce smoothness Algorithm knows all ( ββ β ) bounds to rebalance. such that β οΏ½ ββ is smooth. ββ cost β² 1 βΏ 976052 . Note: one β works for all β . (2001 Bernstein) Algorithm uses ECM to check whether β οΏ½ ββ β is smooth.
ββ cost analysis The factorization factory Finding is slower Sieving is a disaster 1993 Coppersmith: Need to ββ β realistic cost metric. There exists an algorithm such that β οΏ½ ββ ββ cost β² 2 βΏ 403750 . that factors any integer RAM time β² βΏ with same #bits as β find smooth using ECM. in RAM time β² 1 βΏ 638587 . ββ cost β² 1 βΏ 923000 . Smoothness bound β² 0 βΏ 819290 . algebra is also a disaster. Smaller than before, ββ cost β² 2 βΏ 403750 . so need more ( ββ β ). Semi-fix: Reduce smoothness Algorithm knows all ( ββ β ) ounds to rebalance. such that β οΏ½ ββ is smooth. ββ cost β² 1 βΏ 976052 . Note: one β works for all β . Bernstein) Algorithm uses ECM to check whether β οΏ½ ββ β is smooth.
ββ The factorization factory Finding this algorithm is slower than running disaster 1993 Coppersmith: Need to precompute ββ β metric. There exists an algorithm such that β οΏ½ ββ β² βΏ 403750 . ββ that factors any integer RAM time β² 2 βΏ 006853 with same #bits as β using ECM. in RAM time β² 1 βΏ 638587 . β² βΏ 923000 . ββ Smoothness bound β² 0 βΏ 819290 . also a disaster. Smaller than before, β² βΏ 403750 . ββ so need more ( ββ β ). Reduce smoothness Algorithm knows all ( ββ β ) rebalance. such that β οΏ½ ββ is smooth. β² βΏ 976052 . ββ Note: one β works for all β . Bernstein) Algorithm uses ECM to check whether β οΏ½ ββ β is smooth.
ββ The factorization factory Finding this algorithm is slower than running it. 1993 Coppersmith: Need to precompute all ( ββ β There exists an algorithm such that β οΏ½ ββ is smooth. β² βΏ ββ that factors any integer RAM time β² 2 βΏ 006853 . with same #bits as β ECM. in RAM time β² 1 βΏ 638587 . β² βΏ ββ Smoothness bound β² 0 βΏ 819290 . disaster. Smaller than before, β² βΏ ββ so need more ( ββ β ). othness Algorithm knows all ( ββ β ) such that β οΏ½ ββ is smooth. β² βΏ ββ Note: one β works for all β . Algorithm uses ECM to check whether β οΏ½ ββ β is smooth.
The factorization factory Finding this algorithm is slower than running it. 1993 Coppersmith: Need to precompute all ( ββ β ) There exists an algorithm such that β οΏ½ ββ is smooth. that factors any integer RAM time β² 2 βΏ 006853 . with same #bits as β in RAM time β² 1 βΏ 638587 . Smoothness bound β² 0 βΏ 819290 . Smaller than before, so need more ( ββ β ). Algorithm knows all ( ββ β ) such that β οΏ½ ββ is smooth. Note: one β works for all β . Algorithm uses ECM to check whether β οΏ½ ββ β is smooth.
The factorization factory Finding this algorithm is slower than running it. 1993 Coppersmith: Need to precompute all ( ββ β ) There exists an algorithm such that β οΏ½ ββ is smooth. that factors any integer RAM time β² 2 βΏ 006853 . with same #bits as β in RAM time β² 1 βΏ 638587 . Standard conversion of precomputation into batching: Smoothness bound β² 0 βΏ 819290 . if there are enough targets, Smaller than before, more than β² 0 βΏ 368266 , so need more ( ββ β ). then precomputation cost Algorithm knows all ( ββ β ) becomes negligible. such that β οΏ½ ββ is smooth. Note: one β works for all β . Algorithm uses ECM to check whether β οΏ½ ββ β is smooth.
The factorization factory Finding this algorithm is slower than running it. 1993 Coppersmith: Need to precompute all ( ββ β ) There exists an algorithm such that β οΏ½ ββ is smooth. that factors any integer RAM time β² 2 βΏ 006853 . with same #bits as β in RAM time β² 1 βΏ 638587 . Standard conversion of precomputation into batching: Smoothness bound β² 0 βΏ 819290 . if there are enough targets, Smaller than before, more than β² 0 βΏ 368266 , so need more ( ββ β ). then precomputation cost Algorithm knows all ( ββ β ) becomes negligible. such that β οΏ½ ββ is smooth. The big problem: Coppersmithβs Note: one β works for all β . algorithm has size β² 1 βΏ 638587 . Algorithm uses ECM to check Huge ββ cost; useless in reality. whether β οΏ½ ββ β is smooth.
factorization factory Finding this algorithm Batch NFS is slower than running it. Coppersmith: Goal: Optimize ββ Need to precompute all ( ββ β ) exists an algorithm 1. Generate ββ β such that β οΏ½ ββ is smooth. factors any integer RAM time β² 2 βΏ 006853 . Test β οΏ½ ββ same #bits as β time β² 1 βΏ 638587 . 2. Make β Standard conversion of close to ββ β precomputation into batching: othness bound β² 0 βΏ 819290 . When smo β οΏ½ ββ if there are enough targets, Smaller than before, more than β² 0 βΏ 368266 , test each β οΏ½ ββ β need more ( ββ β ). then precomputation cost 3. After rithm knows all ( ββ β ) becomes negligible. reorganize: β that β οΏ½ ββ is smooth. relevant ββ β The big problem: Coppersmithβs one β works for all β . algorithm has size β² 1 βΏ 638587 . 4. Linear rithm uses ECM to check Huge ββ cost; useless in reality. whether β οΏ½ ββ β is smooth.
ization factory Finding this algorithm Batch NFS is slower than running it. ersmith: Goal: Optimize ββ Need to precompute all ( ββ β ) algorithm 1. Generate ( ββ β ) such that β οΏ½ ββ is smooth. integer RAM time β² 2 βΏ 006853 . Test β οΏ½ ββ for smo as β β² βΏ 638587 . 2. Make many copies β Standard conversion of close to each ( ββ β ) precomputation into batching: ound β² 0 βΏ 819290 . When smooth β οΏ½ ββ if there are enough targets, efore, more than β² 0 βΏ 368266 , test each β οΏ½ ββ β ββ β ). then precomputation cost 3. After all smooths ws all ( ββ β ) becomes negligible. reorganize: for each β β οΏ½ ββ is smooth. relevant ( ββ β ) close The big problem: Coppersmithβs β rks for all β . algorithm has size β² 1 βΏ 638587 . 4. Linear algebra. ECM to check Huge ββ cost; useless in reality. β οΏ½ ββ β is smooth.
Finding this algorithm Batch NFS is slower than running it. Goal: Optimize ββ asymptotics. Need to precompute all ( ββ β ) 1. Generate ( ββ β ) in parallel. such that β οΏ½ ββ is smooth. RAM time β² 2 βΏ 006853 . Test β οΏ½ ββ for smoothness. β β² βΏ 2. Make many copies of eac β Standard conversion of close to each ( ββ β ) generato precomputation into batching: β² βΏ 819290 . When smooth β οΏ½ ββ is found, if there are enough targets, more than β² 0 βΏ 368266 , test each β οΏ½ ββ β for smoothness. ββ β then precomputation cost 3. After all smooths are found, ββ β becomes negligible. reorganize: for each β , bring β οΏ½ ββ oth. relevant ( ββ β ) close together. The big problem: Coppersmithβs β β . algorithm has size β² 1 βΏ 638587 . 4. Linear algebra. check Huge ββ cost; useless in reality. β οΏ½ ββ β oth.
Finding this algorithm Batch NFS is slower than running it. Goal: Optimize ββ asymptotics. Need to precompute all ( ββ β ) 1. Generate ( ββ β ) in parallel. such that β οΏ½ ββ is smooth. RAM time β² 2 βΏ 006853 . Test β οΏ½ ββ for smoothness. 2. Make many copies of each β , Standard conversion of close to each ( ββ β ) generator. precomputation into batching: When smooth β οΏ½ ββ is found, if there are enough targets, more than β² 0 βΏ 368266 , test each β οΏ½ ββ β for smoothness. then precomputation cost 3. After all smooths are found, becomes negligible. reorganize: for each β , bring relevant ( ββ β ) close together. The big problem: Coppersmithβs algorithm has size β² 1 βΏ 638587 . 4. Linear algebra. Huge ββ cost; useless in reality.
Finding this algorithm Batch NFS Generate ( ββ β ). ββ β ββ β ββ β er than running it. Is β οΏ½ ββ β οΏ½ ββ β οΏ½ ββ β οΏ½ ββ Goal: Optimize ββ asymptotics. smooth? to precompute all ( ββ β ) If so, store. 1. Generate ( ββ β ) in parallel. that β οΏ½ ββ is smooth. Repeat. time β² 2 βΏ 006853 . Test β οΏ½ ββ for smoothness. Generate ( ββ β ). ββ β ββ β ββ β Is β οΏ½ ββ β οΏ½ ββ β οΏ½ ββ β οΏ½ ββ smooth? 2. Make many copies of each β , Standard conversion of If so, store. close to each ( ββ β ) generator. recomputation into batching: Repeat. Generate ( ββ β ). ββ β ββ β ββ β When smooth β οΏ½ ββ is found, there are enough targets, Is β οΏ½ ββ β οΏ½ ββ β οΏ½ ββ β οΏ½ ββ than β² 0 βΏ 368266 , test each β οΏ½ ββ β for smoothness. smooth? If so, store. recomputation cost Repeat. 3. After all smooths are found, Generate ( ββ β ). ββ β ββ β ββ β ecomes negligible. reorganize: for each β , bring Is β οΏ½ ββ β οΏ½ ββ β οΏ½ ββ β οΏ½ ββ smooth? relevant ( ββ β ) close together. big problem: Coppersmithβs If so, store. rithm has size β² 1 βΏ 638587 . Repeat. 4. Linear algebra. ββ cost; useless in reality.
algorithm Batch NFS Generate ( ββ β ). Generate ( ββ β ). Generate ββ β ββ β running it. Is β οΏ½ ββ Is β οΏ½ ββ β οΏ½ ββ β οΏ½ ββ Goal: Optimize ββ asymptotics. smooth? smooth? recompute all ( ββ β ) If so, store. If so, store. 1. Generate ( ββ β ) in parallel. β οΏ½ ββ is smooth. Repeat. Repeat. β² βΏ 006853 . Test β οΏ½ ββ for smoothness. Generate ( ββ β ). Generate ( ββ β ). Generate ββ β ββ β Is β οΏ½ ββ Is β οΏ½ ββ β οΏ½ ββ β οΏ½ ββ smooth? smooth? 2. Make many copies of each β , conversion of If so, store. If so, store. close to each ( ββ β ) generator. into batching: Repeat. Repeat. Generate ( ββ β ). Generate ( ββ β ). Generate ββ β ββ β When smooth β οΏ½ ββ is found, enough targets, Is β οΏ½ ββ Is β οΏ½ ββ β οΏ½ ββ β οΏ½ ββ β² βΏ 368266 , test each β οΏ½ ββ β for smoothness. smooth? smooth? If so, store. If so, store. computation cost Repeat. Repeat. 3. After all smooths are found, Generate ( ββ β ). Generate ( ββ β ). Generate ββ β ββ β negligible. reorganize: for each β , bring Is β οΏ½ ββ Is β οΏ½ ββ β οΏ½ ββ β οΏ½ ββ smooth? smooth? relevant ( ββ β ) close together. roblem: Coppersmithβs If so, store. If so, store. size β² 1 βΏ 638587 . Repeat. Repeat. 4. Linear algebra. ββ useless in reality.
Batch NFS Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Generate ββ β Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Goal: Optimize ββ asymptotics. smooth? smooth? smooth? ββ β ) If so, store. If so, store. If so, store. If 1. Generate ( ββ β ) in parallel. β οΏ½ ββ oth. Repeat. Repeat. Repeat. β² βΏ Test β οΏ½ ββ for smoothness. Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Generate ββ β Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ smooth? smooth? smooth? 2. Make many copies of each β , If so, store. If so, store. If so, store. If close to each ( ββ β ) generator. batching: Repeat. Repeat. Repeat. Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Generate ββ β When smooth β οΏ½ ββ is found, rgets, Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ β² βΏ test each β οΏ½ ββ β for smoothness. smooth? smooth? smooth? If so, store. If so, store. If so, store. If Repeat. Repeat. Repeat. 3. After all smooths are found, Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Generate ββ β reorganize: for each β , bring Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ smooth? smooth? smooth? relevant ( ββ β ) close together. rsmithβs If so, store. If so, store. If so, store. If β² βΏ 638587 . Repeat. Repeat. Repeat. 4. Linear algebra. ββ reality.
Batch NFS Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Goal: Optimize ββ asymptotics. smooth? smooth? smooth? smooth? If so, store. If so, store. If so, store. If so, store. 1. Generate ( ββ β ) in parallel. Repeat. Repeat. Repeat. Repeat. Test β οΏ½ ββ for smoothness. Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ smooth? smooth? smooth? smooth? 2. Make many copies of each β , If so, store. If so, store. If so, store. If so, store. close to each ( ββ β ) generator. Repeat. Repeat. Repeat. Repeat. Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). When smooth β οΏ½ ββ is found, Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ test each β οΏ½ ββ β for smoothness. smooth? smooth? smooth? smooth? If so, store. If so, store. If so, store. If so, store. Repeat. Repeat. Repeat. Repeat. 3. After all smooths are found, Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). reorganize: for each β , bring Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ smooth? smooth? smooth? smooth? relevant ( ββ β ) close together. If so, store. If so, store. If so, store. If so, store. Repeat. Repeat. Repeat. Repeat. 4. Linear algebra.
NFS Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Is β οΏ½ ββ 1 β οΏ½ ββ β οΏ½ ββ β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ smooth? Optimize ββ asymptotics. smooth? smooth? smooth? smooth? If so, store. If so, store. If so, store. If so, store. If so, store. Send ( ββ β ). ββ β ββ β ββ β Generate ( ββ β ) in parallel. Repeat. Repeat. Repeat. Repeat. right. Repeat. β οΏ½ ββ for smoothness. Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Is β οΏ½ ββ 5 β οΏ½ ββ β οΏ½ ββ β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ smooth? smooth? smooth? smooth? smooth? If so, store. Make many copies of each β , If so, store. If so, store. If so, store. If so, store. Send ( ββ β ). ββ β ββ β ββ β to each ( ββ β ) generator. Repeat. Repeat. Repeat. Repeat. up. Repeat. Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Is β οΏ½ ββ 9 β οΏ½ ββ β οΏ½ ββ β οΏ½ ββ smooth β οΏ½ ββ is found, Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ smooth? each β οΏ½ ββ β for smoothness. smooth? smooth? smooth? smooth? If so, store. If so, store. If so, store. If so, store. If so, store. Send ( ββ β ). ββ β ββ β ββ β Repeat. Repeat. Repeat. Repeat. right. Repeat. After all smooths are found, Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Is β οΏ½ ββ 13 β οΏ½ ββ β οΏ½ ββ β οΏ½ ββ rganize: for each β , bring Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ smooth? smooth? smooth? smooth? smooth? If so, store. relevant ( ββ β ) close together. If so, store. If so, store. If so, store. If so, store. Send ( ββ β ). ββ β ββ β ββ β Repeat. Repeat. Repeat. Repeat. up. Repeat. Linear algebra.
Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Is β οΏ½ ββ 1 Is β οΏ½ ββ 2 β οΏ½ ββ β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ smooth? smooth? ββ asymptotics. smooth? smooth? smooth? smooth? If so, store. If so, store. If so, store. If so, store. If so, store. If so, store. Send ( ββ β ). Send ( ββ β ). ββ β ββ β ββ β ) in parallel. Repeat. Repeat. Repeat. Repeat. right. Repeat. right. Repeat. right. smoothness. β οΏ½ ββ Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Is β οΏ½ ββ 5 Is β οΏ½ ββ 6 β οΏ½ ββ β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ smooth? smooth? smooth? smooth? smooth? smooth? If so, store. If so, store. copies of each β , If so, store. If so, store. If so, store. If so, store. Send ( ββ β ). Send ( ββ β ). ββ β ββ β ββ β ) generator. Repeat. Repeat. Repeat. Repeat. up. Repeat. left. Repeat. Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Is β οΏ½ ββ 9 Is β οΏ½ ββ 10 β οΏ½ ββ β οΏ½ ββ β οΏ½ ββ is found, Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ smooth? smooth? β οΏ½ ββ β for smoothness. smooth? smooth? smooth? smooth? If so, store. If so, store. If so, store. If so, store. If so, store. If so, store. Send ( ββ β ). Send ( ββ β ). ββ β ββ β Repeat. Repeat. Repeat. Repeat. right. Repeat. right. Repeat. right. oths are found, Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Is β οΏ½ ββ 13 Is β οΏ½ ββ 14 β οΏ½ ββ β οΏ½ ββ each β , bring Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ smooth? smooth? smooth? smooth? smooth? smooth? If so, store. If so, store. ββ β close together. If so, store. If so, store. If so, store. If so, store. Send ( ββ β ). Send ( ββ β ). ββ β ββ β Repeat. Repeat. Repeat. Repeat. up. Repeat. left. Repeat. ra.
Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Is β οΏ½ ββ 1 Is β οΏ½ ββ 2 Is β οΏ½ ββ 3 Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ smooth? smooth? smooth? ββ asymptotics. smooth? smooth? smooth? smooth? If so, store. If so, store. If so, store. If If so, store. If so, store. If so, store. If so, store. Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). Send ββ β ββ β rallel. Repeat. Repeat. Repeat. Repeat. right. Repeat. right. Repeat. right. Repeat. down. othness. β οΏ½ ββ Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Is β οΏ½ ββ 5 Is β οΏ½ ββ 6 Is β οΏ½ ββ 7 Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ smooth? smooth? smooth? smooth? smooth? smooth? smooth? If so, store. If so, store. If so, store. If ach β , If so, store. If so, store. If so, store. If so, store. Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). Send ββ β ββ β generator. Repeat. Repeat. Repeat. Repeat. up. Repeat. left. Repeat. left. Repeat. left. Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Is β οΏ½ ββ 9 Is β οΏ½ ββ 10 Is β οΏ½ ββ 11 Is β οΏ½ ββ found, β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ smooth? smooth? smooth? β οΏ½ ββ β othness. smooth? smooth? smooth? smooth? If so, store. If so, store. If so, store. If If so, store. If so, store. If so, store. If so, store. Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). Send ββ β Repeat. Repeat. Repeat. Repeat. right. Repeat. right. Repeat. right. Repeat. down. found, Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Is β οΏ½ ββ 13 Is β οΏ½ ββ 14 Is β οΏ½ ββ 15 Is β οΏ½ ββ ring β Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ smooth? smooth? smooth? smooth? smooth? smooth? smooth? If so, store. If so, store. If so, store. If ββ β together. If so, store. If so, store. If so, store. If so, store. Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). Send ββ β Repeat. Repeat. Repeat. Repeat. up. Repeat. left. Repeat. left. Repeat. left.
Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Is β οΏ½ ββ 1 Is β οΏ½ ββ 2 Is β οΏ½ ββ 3 Is β οΏ½ ββ 4 Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ smooth? smooth? smooth? smooth? smooth? smooth? smooth? smooth? If so, store. If so, store. If so, store. If so, store. If so, store. If so, store. If so, store. If so, store. Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). Repeat. Repeat. Repeat. Repeat. right. Repeat. right. Repeat. right. Repeat. down. Repeat. Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Is β οΏ½ ββ 5 Is β οΏ½ ββ 6 Is β οΏ½ ββ 7 Is β οΏ½ ββ 8 Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ smooth? smooth? smooth? smooth? smooth? smooth? smooth? smooth? If so, store. If so, store. If so, store. If so, store. If so, store. If so, store. If so, store. If so, store. Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). Repeat. Repeat. Repeat. Repeat. up. Repeat. left. Repeat. left. Repeat. left. Repeat. Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Is β οΏ½ ββ 9 Is β οΏ½ ββ 10 Is β οΏ½ ββ 11 Is β οΏ½ ββ 12 Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ smooth? smooth? smooth? smooth? smooth? smooth? smooth? smooth? If so, store. If so, store. If so, store. If so, store. If so, store. If so, store. If so, store. If so, store. Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). Repeat. Repeat. Repeat. Repeat. right. Repeat. right. Repeat. right. Repeat. down. Repeat. Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Is β οΏ½ ββ 13 Is β οΏ½ ββ 14 Is β οΏ½ ββ 15 Is β οΏ½ ββ 16 Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ smooth? smooth? smooth? smooth? smooth? smooth? smooth? smooth? If so, store. If so, store. If so, store. If so, store. If so, store. If so, store. If so, store. If so, store. Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). Repeat. Repeat. Repeat. Repeat. up. Repeat. left. Repeat. left. Repeat. left. Repeat.
ββ β ). Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Is β οΏ½ ββ 1 Is β οΏ½ ββ 2 Is β οΏ½ ββ 3 Is β οΏ½ ββ 4 β 1 β β 2 β β 3 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ smooth? smooth? smooth? smooth? β 5 β β 6 β β 7 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β smooth? smooth? smooth? If so, store. If so, store. If so, store. If so, store. β 9 β β 10 β β 11 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β . If so, store. If so, store. If so, store. Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). β 13 β β 14 β β 15 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β Repeat. Repeat. Repeat. right. Repeat. right. Repeat. right. Repeat. down. Repeat. β 1 β β 2 β β 3 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β ββ β ). Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Is β οΏ½ ββ 5 Is β οΏ½ ββ 6 Is β οΏ½ ββ 7 Is β οΏ½ ββ 8 β 5 β β 6 β β 7 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ smooth? smooth? smooth? smooth? β οΏ½ ββ β 9 β β 10 β β 11 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β smooth? smooth? smooth? If so, store. If so, store. If so, store. If so, store. β 13 β β 14 β β 15 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β . If so, store. If so, store. If so, store. Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). β 1 β β 2 β β 3 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β Repeat. Repeat. Repeat. up. Repeat. left. Repeat. left. Repeat. left. Repeat. β 5 β β 6 β β 7 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β ββ β ). Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Is β οΏ½ ββ 9 Is β οΏ½ ββ 10 Is β οΏ½ ββ 11 Is β οΏ½ ββ 12 β 9 β β 10 β β 11 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ smooth? smooth? smooth? smooth? β οΏ½ ββ β 13 β β 14 β β 15 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β smooth? smooth? smooth? If so, store. If so, store. If so, store. If so, store. β 1 β β 2 β β 3 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β . If so, store. If so, store. If so, store. Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). β 5 β β 6 β β 7 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β Repeat. Repeat. Repeat. right. Repeat. right. Repeat. right. Repeat. down. Repeat. β 9 β β 10 β β 11 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β ββ β ). Generate ( ββ β ). Generate ( ββ β ). Generate ( ββ β ). Is β οΏ½ ββ 13 Is β οΏ½ ββ 14 Is β οΏ½ ββ 15 Is β οΏ½ ββ 16 β 13 β β 14 β β 15 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β Is β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ smooth? smooth? smooth? smooth? β 1 β β 2 β β 3 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β οΏ½ ββ smooth? smooth? smooth? If so, store. If so, store. If so, store. If so, store. β 5 β β 6 β β 7 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β . If so, store. If so, store. If so, store. Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). β 9 β β 10 β β 11 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β Repeat. Repeat. Repeat. up. Repeat. left. Repeat. left. Repeat. left. Repeat. β 13 β β 14 β β 15 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β
Generate ( ββ β ). Generate ( ββ β ). Is β οΏ½ ββ 1 Is β οΏ½ ββ 2 Is β οΏ½ ββ 3 Is β οΏ½ ββ 4 ββ β ββ β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β β β β β β β β β β β β β β β β β β β β β β β β β οΏ½ ββ β οΏ½ ββ Is β οΏ½ ββ Is β οΏ½ ββ smooth? smooth? smooth? smooth? β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β β β β β β β β β β β β β β β β β β β β β β β β smooth? smooth? If so, store. If so, store. If so, store. If so, store. β 9 β β 10 β β 11 β β 12 β 9 β β 10 β β β β β β β β β β β β β β β β β β β β β β β β β If so, store. If so, store. Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). β 13 β β 14 β β 15 β β 16 β 13 β β 14 β β β β β β β β β β β β β β β β β β β β β β β β β Repeat. Repeat. right. Repeat. right. Repeat. right. Repeat. down. Repeat. β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β β β β β β β β β β β β β β β β β β β β β β β β Generate ( ββ β ). Generate ( ββ β ). Is β οΏ½ ββ 5 Is β οΏ½ ββ 6 Is β οΏ½ ββ 7 Is β οΏ½ ββ 8 ββ β ββ β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β β β β β β β β β β β β β β β β β β β β β β β β Is β οΏ½ ββ Is β οΏ½ ββ smooth? smooth? smooth? smooth? β οΏ½ ββ β οΏ½ ββ β 9 β β 10 β β 11 β β 12 β 9 β β 10 β β β β β β β β β β β β β β β β β β β β β β β β β smooth? smooth? If so, store. If so, store. If so, store. If so, store. β 13 β β 14 β β 15 β β 16 β 13 β β 14 β β β β β β β β β β β β β β β β β β β β β β β β β If so, store. If so, store. Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β β β β β β β β β β β β β β β β β β β β β β β β Repeat. Repeat. up. Repeat. left. Repeat. left. Repeat. left. Repeat. β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β β β β β β β β β β β β β β β β β β β β β β β β Generate ( ββ β ). Generate ( ββ β ). Is β οΏ½ ββ 9 Is β οΏ½ ββ 10 Is β οΏ½ ββ 11 Is β οΏ½ ββ 12 ββ β ββ β β 9 β β 10 β β 11 β β 12 β 9 β β 10 β β β β β β β β β β β β β β β β β β β β β β β β β Is β οΏ½ ββ Is β οΏ½ ββ smooth? smooth? smooth? smooth? β οΏ½ ββ β οΏ½ ββ β 13 β β 14 β β 15 β β 16 β 13 β β 14 β β β β β β β β β β β β β β β β β β β β β β β β β smooth? smooth? If so, store. If so, store. If so, store. If so, store. β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β β β β β β β β β β β β β β β β β β β β β β β β If so, store. If so, store. Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β β β β β β β β β β β β β β β β β β β β β β β β Repeat. Repeat. right. Repeat. right. Repeat. right. Repeat. down. Repeat. β 9 β β 10 β β 11 β β 12 β 9 β β 10 β β β β β β β β β β β β β β β β β β β β β β β β β Generate ( ββ β ). Generate ( ββ β ). Is β οΏ½ ββ 13 Is β οΏ½ ββ 14 Is β οΏ½ ββ 15 Is β οΏ½ ββ 16 ββ β ββ β β 13 β β 14 β β 15 β β 16 β 13 β β 14 β β β β β β β β β β β β β β β β β β β β β β β β β Is β οΏ½ ββ Is β οΏ½ ββ smooth? smooth? smooth? smooth? β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β β β β β β β β β β β β β β β β β β β β β β β β β οΏ½ ββ β οΏ½ ββ smooth? smooth? If so, store. If so, store. If so, store. If so, store. β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β β β β β β β β β β β β β β β β β β β β β β β β If so, store. If so, store. Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). β 9 β β 10 β β 11 β β 12 β 9 β β 10 β β β β β β β β β β β β β β β β β β β β β β β β β Repeat. Repeat. up. Repeat. left. Repeat. left. Repeat. left. Repeat. β 13 β β 14 β β 15 β β 16 β 13 β β 14 β β β β β β β β β β β β β β β β β β β β β β β β β
Generate ( ββ β ). Is β οΏ½ ββ 1 Is β οΏ½ ββ 2 Is β οΏ½ ββ 3 Is β οΏ½ ββ 4 ββ β ββ β ββ β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β 1 β β β β β β β β β β β β β β β β β β β β β οΏ½ ββ β οΏ½ ββ β οΏ½ ββ Is β οΏ½ ββ smooth? smooth? smooth? smooth? β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β 5 β β β β β β β β β β β β β β β β β β β β smooth? If so, store. If so, store. If so, store. If so, store. β 9 β β 10 β β 11 β β 12 β 9 β β 10 β β 11 β β 12 β 9 β β β β β β β β β β β β β β β β β β β β If so, store. Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). β 13 β β 14 β β 15 β β 16 β 13 β β 14 β β 15 β β 16 β 13 β β β β β β β β β β β β β β β β β β β β Repeat. right. Repeat. right. Repeat. right. Repeat. down. Repeat. β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β 1 β β β β β β β β β β β β β β β β β β β β Generate ( ββ β ). Is β οΏ½ ββ 5 Is β οΏ½ ββ 6 Is β οΏ½ ββ 7 Is β οΏ½ ββ 8 ββ β ββ β ββ β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β 5 β β β β β β β β β β β β β β β β β β β β Is β οΏ½ ββ smooth? smooth? smooth? smooth? β οΏ½ ββ β οΏ½ ββ β οΏ½ ββ β 9 β β 10 β β 11 β β 12 β 9 β β 10 β β 11 β β 12 β 9 β β β β β β β β β β β β β β β β β β β β smooth? If so, store. If so, store. If so, store. If so, store. β 13 β β 14 β β 15 β β 16 β 13 β β 14 β β 15 β β 16 β 13 β β β β β β β β β β β β β β β β β β β β If so, store. Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β 1 β β β β β β β β β β β β β β β β β β β β Repeat. up. Repeat. left. Repeat. left. Repeat. left. Repeat. β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β 5 β β β β β β β β β β β β β β β β β β β β Generate ( ββ β ). Is β οΏ½ ββ 9 Is β οΏ½ ββ 10 Is β οΏ½ ββ 11 Is β οΏ½ ββ 12 ββ β ββ β ββ β β 9 β β 10 β β 11 β β 12 β 9 β β 10 β β 11 β β 12 β 9 β β β β β β β β β β β β β β β β β β β β Is β οΏ½ ββ smooth? smooth? smooth? smooth? β οΏ½ ββ β οΏ½ ββ β οΏ½ ββ β 13 β β 14 β β 15 β β 16 β 13 β β 14 β β 15 β β 16 β 13 β β β β β β β β β β β β β β β β β β β β smooth? If so, store. If so, store. If so, store. If so, store. β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β 1 β β β β β β β β β β β β β β β β β β β β If so, store. Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β 5 β β β β β β β β β β β β β β β β β β β β Repeat. right. Repeat. right. Repeat. right. Repeat. down. Repeat. β 9 β β 10 β β 11 β β 12 β 9 β β 10 β β 11 β β 12 β 9 β β β β β β β β β β β β β β β β β β β β Generate ( ββ β ). Is β οΏ½ ββ 13 Is β οΏ½ ββ 14 Is β οΏ½ ββ 15 Is β οΏ½ ββ 16 ββ β ββ β ββ β β 13 β β 14 β β 15 β β 16 β 13 β β 14 β β 15 β β 16 β 13 β β β β β β β β β β β β β β β β β β β β Is β οΏ½ ββ smooth? smooth? smooth? smooth? β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β 1 β β β β β β β β β β β β β β β β β β β β β οΏ½ ββ β οΏ½ ββ β οΏ½ ββ smooth? If so, store. If so, store. If so, store. If so, store. β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β 5 β β β β β β β β β β β β β β β β β β β β If so, store. Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). β 9 β β 10 β β 11 β β 12 β 9 β β 10 β β 11 β β 12 β 9 β β β β β β β β β β β β β β β β β β β β Repeat. up. Repeat. left. Repeat. left. Repeat. left. Repeat. β 13 β β 14 β β 15 β β 16 β 13 β β 14 β β 15 β β 16 β 13 β β β β β β β β β β β β β β β β β β β β
Is β οΏ½ ββ 1 Is β οΏ½ ββ 2 Is β οΏ½ ββ 3 Is β οΏ½ ββ 4 β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β smooth? smooth? smooth? smooth? β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β If so, store. If so, store. If so, store. If so, store. β 9 β β 10 β β 11 β β 12 β 9 β β 10 β β 11 β β 12 β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β β β β Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). β 13 β β 14 β β 15 β β 16 β 13 β β 14 β β 15 β β 16 β 13 β β 14 β β 15 β β 16 β β β β β β β β β β β β β β right. Repeat. right. Repeat. right. Repeat. down. Repeat. β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β Is β οΏ½ ββ 5 Is β οΏ½ ββ 6 Is β οΏ½ ββ 7 Is β οΏ½ ββ 8 β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β smooth? smooth? smooth? smooth? β 9 β β 10 β β 11 β β 12 β 9 β β 10 β β 11 β β 12 β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β β β β If so, store. If so, store. If so, store. If so, store. β 13 β β 14 β β 15 β β 16 β 13 β β 14 β β 15 β β 16 β 13 β β 14 β β 15 β β 16 β β β β β β β β β β β β β β Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β up. Repeat. left. Repeat. left. Repeat. left. Repeat. β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β Is β οΏ½ ββ 9 Is β οΏ½ ββ 10 Is β οΏ½ ββ 11 Is β οΏ½ ββ 12 β 9 β β 10 β β 11 β β 12 β 9 β β 10 β β 11 β β 12 β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β β β β smooth? smooth? smooth? smooth? β 13 β β 14 β β 15 β β 16 β 13 β β 14 β β 15 β β 16 β 13 β β 14 β β 15 β β 16 β β β β β β β β β β β β β β If so, store. If so, store. If so, store. If so, store. β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β right. Repeat. right. Repeat. right. Repeat. down. Repeat. β 9 β β 10 β β 11 β β 12 β 9 β β 10 β β 11 β β 12 β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β β β β Is β οΏ½ ββ 13 Is β οΏ½ ββ 14 Is β οΏ½ ββ 15 Is β οΏ½ ββ 16 β 13 β β 14 β β 15 β β 16 β 13 β β 14 β β 15 β β 16 β 13 β β 14 β β 15 β β 16 β β β β β β β β β β β β β β smooth? smooth? smooth? smooth? β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β If so, store. If so, store. If so, store. If so, store. β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). β 9 β β 10 β β 11 β β 12 β 9 β β 10 β β 11 β β 12 β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β β β β up. Repeat. left. Repeat. left. Repeat. left. Repeat. β 13 β β 14 β β 15 β β 16 β 13 β β 14 β β 15 β β 16 β 13 β β 14 β β 15 β β 16 β β β β β β β β β β β β β β
Is β οΏ½ ββ 2 Is β οΏ½ ββ 3 Is β οΏ½ ββ 4 β οΏ½ ββ β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β β β β β β β β smooth? smooth? smooth? β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β . If so, store. If so, store. If so, store. β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β β β β β β β β ββ β ). Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). β 13 β β 14 β β 15 β β 16 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β β β β β β β β β β β β eat. right. Repeat. right. Repeat. down. Repeat. β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β β β β β β β β Is β οΏ½ ββ 6 Is β οΏ½ ββ 7 Is β οΏ½ ββ 8 β οΏ½ ββ β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β smooth? smooth? smooth? β β β β 9 β β 10 β β 11 β β 12 β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β β β β β β β β . If so, store. If so, store. If so, store. β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β β β β β β β β β β β β β ββ β ). Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β β β β β β β β eat. left. Repeat. left. Repeat. left. Repeat. β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β Is β οΏ½ ββ 10 Is β οΏ½ ββ 11 Is β οΏ½ ββ 12 β οΏ½ ββ β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β β β β β β β β smooth? smooth? smooth? β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β β β β β β β β β β β β β . If so, store. If so, store. If so, store. β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β ββ β ). Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β β β β β β β eat. right. Repeat. right. Repeat. down. Repeat. β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β β β β β β β β β β β Is β οΏ½ ββ 14 Is β οΏ½ ββ 15 Is β οΏ½ ββ 16 β οΏ½ ββ β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β β β β β β β β β β β β smooth? smooth? smooth? β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β β β β β β β β . If so, store. If so, store. If so, store. β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β ββ β ). Send ( ββ β ). Send ( ββ β ). Send ( ββ β ). β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β β β β β β β β β β β eat. left. Repeat. left. Repeat. left. Repeat. β 13 β β 14 β β 15 β β 16 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β β β β β β β β β β β β
Is β οΏ½ ββ 3 Is β οΏ½ ββ 4 β οΏ½ ββ β οΏ½ ββ β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β β β β β β β β smooth? smooth? β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β If so, store. If so, store. β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β 9 β β 10 β β 11 β β β β β β β β β Send ( ββ β ). Send ( ββ β ). ββ β ββ β β 13 β β 14 β β 15 β β 16 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β β β β β β β right. Repeat. down. Repeat. β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β β β β β β β β Is β οΏ½ ββ 7 Is β οΏ½ ββ 8 β οΏ½ ββ β οΏ½ ββ β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β smooth? smooth? β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β 9 β β 10 β β 11 β β β β β β If so, store. If so, store. β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β β β β β β β Send ( ββ β ). Send ( ββ β ). β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β β β β β β β β ββ β ββ β left. Repeat. left. Repeat. β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β β β β β β β β Is β οΏ½ ββ 11 Is β οΏ½ ββ 12 β οΏ½ ββ β οΏ½ ββ β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β 9 β β 10 β β 11 β β β β β β β β β smooth? smooth? β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β β β β β β β β If so, store. If so, store. β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β Send ( ββ β ). Send ( ββ β ). ββ β ββ β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β β β β β β β β right. Repeat. down. Repeat. β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β β β β β β β β Is β οΏ½ ββ 15 Is β οΏ½ ββ 16 β οΏ½ ββ β οΏ½ ββ β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β β β β β β β β smooth? smooth? β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β If so, store. If so, store. β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β β β β β β β β Send ( ββ β ). Send ( ββ β ). β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β 9 β β 10 β β 11 β β β β β β β β β ββ β ββ β left. Repeat. left. Repeat. β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β β β β β β β β
Is β οΏ½ ββ 4 β οΏ½ ββ β οΏ½ ββ β οΏ½ ββ β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 smooth? β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 If so, store. β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β Send ( ββ β ). ββ β ββ β ββ β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β down. Repeat. β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β Is β οΏ½ ββ 8 β οΏ½ ββ β οΏ½ ββ β οΏ½ ββ β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β smooth? β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β If so, store. β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β Send ( ββ β ). β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β ββ β ββ β ββ β left. Repeat. β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β Is β οΏ½ ββ 12 β οΏ½ ββ β οΏ½ ββ β οΏ½ ββ β β β β 9 β β 10 β β 11 β β 12 β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β smooth? β 13 β β 14 β β 15 β β 16 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β If so, store. β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β Send ( ββ β ). ββ β ββ β ββ β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β down. Repeat. β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β Is β οΏ½ ββ 16 β οΏ½ ββ β οΏ½ ββ β οΏ½ ββ β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β smooth? β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β If so, store. β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β Send ( ββ β ). β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β ββ β ββ β ββ β left. Repeat. β 13 β β 14 β β 15 β β 16 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β
β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β 9 β β 10 β β 11 β β 12 β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β
Linear algeb β β β β β 3 β β 4 β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β using congruences β β β β β β β β β β β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 ( ββ β ) ( ββ β β β β β β β β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ( ββ β ) ( ββ β β β β β β β β β β β 15 β β 16 β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β ( ββ β ) ( ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β Linear algeb β β β β using congruences β β β β β β β β β β 11 β β 12 β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β ( ββ β ) ( ββ β β β β β β β β β β β 15 β β 16 β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β ( ββ β ) ( ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β ( ββ β ) ( ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β Linear algeb β β β β β β β β β β 11 β β 12 β β β β β β 9 β β 10 β β 11 β β 12 β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β using congruences β β β β β β β β β β 15 β β 16 β β β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β β β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 ( ββ β ) ( ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 ( ββ β ) ( ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β ( ββ β ) ( ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β Linear algebra β β β β β β β β β β 15 β β 16 β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β β β β β β β 3 β β 4 β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β using congruences β β β β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 ( ββ β ) ( ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β 11 β β 12 β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β ( ββ β ) ( ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β 15 β β 16 β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β ( ββ β ) ( ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β
Linear algebra for β 1 Linea β β β β β β β β β β β β β β β β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β using congruences β β β β β β β β β β β β β β β β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β ( ββ β ) ( ββ β ) ( ββ β ) β β β β β β β β β β β β β β β β β β β β 10 β β 11 β β 12 β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ( ββ β ) ( ββ β ) ( ββ β ) β β β β β β β β β β β β β β β β β β β β 14 β β 15 β β 16 β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β 2 β β 3 β β 4 β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 Linear algebra for β 5 Linea β β β using congruences β β β β β β β β β β β β β β β β β β β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β ( ββ β ) ( ββ β ) ( ββ β ) β β β β β β β β β β β β β β β β β β β β 14 β β 15 β β 16 β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β Linear algebra for β 9 Linea β β β β β β β β β β β β β β β β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β β β using congruences β β β β β β β β β β β β β β β β β β β β 14 β β 15 β β 16 β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β Linear algebra for β 13 Linea β β β β β β β β β β β β β β β β β β β β 14 β β 15 β β 16 β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 using congruences β β β β β β β β β β β β β β β β β β β β β β β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β
Linear algebra for β 1 Linear algebra for β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β β using congruences using congruences β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β ββ β ββ β ββ β ββ β ββ β ββ β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 β β β β β β β Linear algebra for β 5 Linear algebra for β β β using congruences using congruences β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β Linear algebra for β 9 Linear algebra for β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 10 β β 11 β β 12 β β β β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β β using congruences using congruences β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β β β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β Linear algebra for β 13 Linear algebra for β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 using congruences using congruences β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β β 5 β β 6 β β 7 β β 8 β β β β β β β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β
Linear algebra for β 1 Linear algebra for β 2 Linear algeb β β β β β β β β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 β β using congruences using congruences using congruences β β β β β β β β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β β β β β β β β β β β β β β β β β β β β β β β 9 β β 10 β β 11 β β 12 β 9 β β 10 β β 11 β β 12 ββ β ββ β ββ β ββ β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β β β β β β β β β β β β β β β β β β β β β β β 13 β β 14 β β 15 β β 16 β 13 β β 14 β β 15 β β 16 ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 Linear algebra for β 5 Linear algebra for β 6 Linear algeb β β using congruences using congruences using congruences β β β β β β β β β β β β β β β β β β β β β β 9 β β 10 β β 11 β β 12 β 9 β β 10 β β 11 β β 12 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β β β β β β β β β β β β β β β β β β β β β β β 13 β β 14 β β 15 β β 16 β 13 β β 14 β β 15 β β 16 ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ββ β ββ β ββ β ββ β Linear algebra for β 9 Linear algebra for β 10 Linear algeb β β β β β β β β β β β β β β β β β β β β β β 9 β β 10 β β 11 β β 12 β 9 β β 10 β β 11 β β 12 β β using congruences using congruences using congruences β β β β β β β β β β β β β β β β β β β β β β 13 β β 14 β β 15 β β 16 β 13 β β 14 β β 15 β β 16 β β β β β β β β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β 9 β β 10 β β 11 β β 12 β 9 β β 10 β β 11 β β 12 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ββ β ββ β ββ β ββ β Linear algebra for β 13 Linear algebra for β 14 Linear algeb β β β β β β β β β β β β β β β β β β β β β β 13 β β 14 β β 15 β β 16 β 13 β β 14 β β 15 β β 16 β β β β β β β β β β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β 1 β β 2 β β 3 β β 4 using congruences using congruences using congruences β β β β β β β β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 β 5 β β 6 β β 7 β β 8 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β 9 β β 10 β β 11 β β 12 β 9 β β 10 β β 11 β β 12 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β 13 β β 14 β β 15 β β 16 β 13 β β 14 β β 15 β β 16 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ββ β ββ β ββ β ββ β
Linear algebra for β 1 Linear algebra for β 2 Linear algeb algebra for β 3 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 β β β β β using congruences using congruences using congruences congruences β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 9 β β 10 β β 11 β β 12 ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 13 β β 14 β β 15 β β 16 ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ( ββ β ) ( ββ β ββ β ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 Linear algebra for β 5 β Linear algebra for β 6 β Linear algeb algebra for β 7 β β β using congruences using congruences using congruences congruences β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 9 β β 10 β β 11 β β 12 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 13 β β 14 β β 15 β β 16 ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β Linear algebra for β 9 Linear algebra for β 10 Linear algeb algebra for β 11 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 9 β β 10 β β 11 β β 12 β β β β β using congruences using congruences using congruences congruences β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 9 β β 10 β β 11 β β 12 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β Linear algebra for β 13 Linear algebra for β 14 Linear algeb algebra for β 15 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 13 β β 14 β β 15 β β 16 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 1 β β 2 β β 3 β β 4 using congruences using congruences using congruences congruences β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 5 β β 6 β β 7 β β 8 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 9 β β 10 β β 11 β β 12 ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ( ββ β ) ( ββ β ββ β ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 13 β β 14 β β 15 β β 16 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β
Linear algebra for β 1 Linear algebra for β 2 Linear algeb algebra for β 3 Linear algebra fo β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 4 β β β β β using congruences using congruences using congruences congruences using congruences β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 8 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 12 ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 16 ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 4 ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ( ββ β ) ( ββ β ββ β ( ββ β ) ( ββ β ) ββ β ( ββ β ) ( ββ β ) ( ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 8 Linear algebra for β 5 β Linear algebra for β 6 β Linear algeb algebra for β 7 β Linear algebra fo β β using congruences using congruences using congruences congruences using congruences β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 12 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 16 ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 4 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 8 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β Linear algebra for β 9 Linear algebra for β 10 Linear algeb algebra for β 11 Linear algebra for β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 12 β β β β using congruences using congruences using congruences congruences using congruences β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 16 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 4 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 8 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 12 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β Linear algebra for β 13 Linear algebra for β 14 Linear algeb algebra for β 15 Linear algebra for β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 16 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 4 using congruences using congruences using congruences congruences using congruences β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 8 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 12 ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ( ββ β ) ( ββ β ββ β ( ββ β ) ( ββ β ) ββ β ( ββ β ) ( ββ β ) ( ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β 16 ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β
Linear algebra for β 1 Linear algebra for β 2 Linear algeb algebra for β 3 Linear algebra for β 4 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β using congruences using congruences using congruences congruences using congruences β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ( ββ β ) ( ββ β ββ β ( ββ β ) ( ββ β ) ββ β ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β Linear algebra for β 5 β Linear algebra for β 6 β Linear algeb algebra for β 7 β Linear algebra for β 8 β using congruences using congruences using congruences congruences using congruences β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β Linear algebra for β 9 Linear algebra for β 10 Linear algeb algebra for β 11 Linear algebra for β 12 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β using congruences using congruences using congruences congruences using congruences β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β Linear algebra for β 13 Linear algebra for β 14 Linear algeb algebra for β 15 Linear algebra for β 16 β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β using congruences using congruences using congruences congruences using congruences β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ( ββ β ) ( ββ β ββ β ( ββ β ) ( ββ β ) ββ β ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ( ββ β ) ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β ββ β
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