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Gap-Hamming Lower Bound March 27, 2009 Lower Bounds for Gap-Hamming-Distance and Consequences for Data Stream Algorithms Amit Chakrabarti (Joint work with Joshua Brody) Dartmouth College DIMACS/DyDAn Workshop, March 2009 Amit Chakrabarti 1


  1. Gap-Hamming Lower Bound March 27, 2009 Lower Bounds for Gap-Hamming-Distance and Consequences for Data Stream Algorithms Amit Chakrabarti (Joint work with Joshua Brody) Dartmouth College DIMACS/DyDAn Workshop, March 2009 Amit Chakrabarti 1

  2. Gap-Hamming Lower Bound March 27, 2009 Status of Certain Streaming Problems, Jan 2009 Problems: • Distinct elements • Frequency moments • Empirical entropy One-pass, randomized, ε -approximate: • Space upper bound: � O ( ε − 2 ) • Space lower bound: � Ω( ε − 2 ) Do multiple passes help? Amit Chakrabarti 2

  3. Gap-Hamming Lower Bound March 27, 2009 Status of Certain Streaming Problems, Jan 2009 Problems: • Distinct elements , F 0 F k = � m i =1 freq( i ) k • Frequency moments , H = � m • Empirical entropy , i =1 (freq( i ) /m ) · log( m/ freq( i )) � � � output � � One-pass, randomized, ε -approximate: answer − 1 � ≤ ε • Space upper bound: � O ( ε − 2 ) • Space lower bound: � Ω( ε − 2 ) Do multiple passes help? Amit Chakrabarti 2-a

  4. Gap-Hamming Lower Bound March 27, 2009 Status of Certain Streaming Problems, Jan 2009 Problems: • Distinct elements , F 0 F k = � m i =1 freq( i ) k • Frequency moments , H = � m • Empirical entropy , i =1 (freq( i ) /m ) · log( m/ freq( i )) � � � output � � One-pass, randomized, ε -approximate: answer − 1 � ≤ ε • Space upper bound: � O ( ε − 2 ) • Space lower bound: � Ω( ε − 2 ) Do multiple passes help? If not, why not? Amit Chakrabarti 2-b

  5. Gap-Hamming Lower Bound March 27, 2009 The Gap-Hamming-Distance Problem Input: Alice gets x ∈ { 0 , 1 } n , Bob gets y ∈ { 0 , 1 } n . Output: 2 + √ n • ghd ( x, y ) = 1 if ∆( x, y ) > n 2 − √ n • ghd ( x, y ) = 0 if ∆( x, y ) < n Problem: Design randomized, constant error protocol to solve this Cost: Worst case number of bits communicated x = 0 1 0 0 1 0 1 1 0 0 0 1 y = 0 0 0 0 0 0 1 1 1 0 0 1 √ √ n = 12; ∆( x, y ) = 3 ∈ [6 − 12 , 6 + 12] Amit Chakrabarti 3

  6. Gap-Hamming Lower Bound March 27, 2009 The Reductions E.g., Distinct Elements (Other problems: similar) x = 0 1 0 0 1 0 1 1 0 0 0 1 ) ) ) 0 0 1 ) ) ) ) ) ) ) ) ) 0 1 0 0 1 0 0 0 0 , , , 0 1 2 ! : , , , , , , , , , 1 2 3 4 5 6 9 8 9 1 1 1 ( ( ( ( ( ( ( ( ( ( ( ( y = 0 0 0 0 0 0 1 1 1 0 0 1 ) ) ) 0 0 1 ) ) ) ) ) ) ) ) ) 0 0 0 0 0 0 0 0 1 , , , 0 1 2 " : , , , , , , , , , 1 2 3 4 5 6 9 8 9 1 1 1 ( ( ( ( ( ( ( ( ( ( ( ( Alice: x �− → σ = � (1 , x 1 ) , (2 , x 2 ) , . . . , ( n, x n ) � Bob: y �− → τ = � (1 , y 1 ) , (2 , y 2 ) , . . . , ( n, y n ) �  2 − √ n, or  < 3 n 1 Notice: F 0 ( σ ◦ τ ) = n + ∆( x, y ) = Set ε = √ n . 2 + √ n.  > 3 n Amit Chakrabarti 4

  7. Gap-Hamming Lower Bound March 27, 2009 Communication to Streaming p -pass streaming algorithm = ⇒ (2 p − 1) -round communication protocol messages = memory contents of streaming algorithm And Thus Previous results [Indyk-Woodruff’03] , [Woodruff’04] , [C.-Cormode-McGregor’07] : • For one-round protocols, R → ( ghd ) = Ω( n ) • Implies the � Ω( ε − 2 ) streaming lower bounds Amit Chakrabarti 5

  8. Gap-Hamming Lower Bound March 27, 2009 Communication to Streaming p -pass streaming algorithm = ⇒ (2 p − 1) -round communication protocol messages = memory contents of streaming algorithm And Thus Previous results [Indyk-Woodruff’03] , [Woodruff’04] , [C.-Cormode-McGregor’07] : • For one-round protocols, R → ( ghd ) = Ω( n ) • Implies the � Ω( ε − 2 ) streaming lower bounds Key open questions: • What is the unrestricted randomized complexity R( ghd ) ? • Better algorithm for Distinct Elements (or F k , or H ) using two passes? Amit Chakrabarti 5-a

  9. Gap-Hamming Lower Bound March 27, 2009 Our Results Previous Results (Communication): • One-round (one-way) lower bound: R → ( ghd ) = Ω( n ) [Woodruff’04] • Simplification, clever reduction from index [Jayram-Kumar-Sivakumar] • Multi-round case: R( ghd ) = Ω( √ n ) [Folklore] Amit Chakrabarti 6

  10. Gap-Hamming Lower Bound March 27, 2009 Our Results Previous Results (Communication): • One-round (one-way) lower bound: R → ( ghd ) = Ω( n ) [Woodruff’04] • Simplification, clever reduction from index [Jayram-Kumar-Sivakumar] Hard distribution “contrived,” non-uniform • Multi-round case: R( ghd ) = Ω( √ n ) [Folklore] Amit Chakrabarti 6-a

  11. Gap-Hamming Lower Bound March 27, 2009 Our Results Previous Results (Communication): • One-round (one-way) lower bound: R → ( ghd ) = Ω( n ) [Woodruff’04] • Simplification, clever reduction from index [Jayram-Kumar-Sivakumar] Hard distribution “contrived,” non-uniform • Multi-round case: R( ghd ) = Ω( √ n ) [Folklore] Reduction from disjointness using “repetition code” Hard distribution again far from uniform Amit Chakrabarti 6-b

  12. Gap-Hamming Lower Bound March 27, 2009 Our Results Previous Results (Communication): • One-round (one-way) lower bound: R → ( ghd ) = Ω( n ) [Woodruff’04] • Simplification, clever reduction from index [Jayram-Kumar-Sivakumar] Hard distribution “contrived,” non-uniform • Multi-round case: R( ghd ) = Ω( √ n ) [Folklore] Reduction from disjointness using “repetition code” Hard distribution again far from uniform What we show: • Theorem 1: Ω( n ) lower bound for any O (1) -round protocol Holds under uniform distribution Amit Chakrabarti 6-c

  13. Gap-Hamming Lower Bound March 27, 2009 Our Results Previous Results (Communication): • One-round (one-way) lower bound: R → ( ghd ) = Ω( n ) [Woodruff’04] • Simplification, clever reduction from index [Jayram-Kumar-Sivakumar] Hard distribution “contrived,” non-uniform • Multi-round case: R( ghd ) = Ω( √ n ) [Folklore] Reduction from disjointness using “repetition code” Hard distribution again far from uniform What we show: • Theorem 1: Ω( n ) lower bound for any O (1) -round protocol Holds under uniform distribution • Theorem 2: one-round, deterministic: D → ( ghd ) = n − Θ( √ n log n ) • Theorem 3: R → ( ghd ) = Ω( n ) (simpler proof, uniform distrib) Amit Chakrabarti 6-d

  14. Gap-Hamming Lower Bound March 27, 2009 Technique: Round Elimination Base Case Lemma: There is no “nice” 0 -round ghd protocol. Round Elimination Lemma: If there is a “nice” k -round ghd protocol, then there is a “nice” ( k − 1) -round ghd protocol. • The ( k − 1) -round protocol will be solving a “simpler” problem • Parameters degrade with each round elimination step Amit Chakrabarti 7

  15. Gap-Hamming Lower Bound March 27, 2009 Technique: Round Elimination Base Case Lemma: There is no 0 -round ghd protocol with error < 1 2 . Round Elimination Lemma: If there is a “nice” k -round ghd protocol, then there is a “nice” ( k − 1) -round ghd � protocol. Amit Chakrabarti 7

  16. Gap-Hamming Lower Bound March 27, 2009 Technique: Round Elimination Base Case Lemma: There is no 0 -round ghd protocol with error < 1 2 . Round Elimination Lemma: If there is a “nice” k -round ghd protocol, then there is a “nice” ( k − 1) -round ghd � protocol. • The ( k − 1) -round protocol will be solving a “simpler” problem • Parameters degrade with each round elimination step Amit Chakrabarti 7-a

  17. Gap-Hamming Lower Bound March 27, 2009 Parametrized Gap-Hamming-Distance Problem The problem:  if ∆( x, y ) ≥ n/ 2 + c √ n ,  1 ,   if ∆( x, y ) ≤ n/ 2 − c √ n , ghd c,n ( x, y ) = 0 ,    � , otherwise. Amit Chakrabarti 8

  18. Gap-Hamming Lower Bound March 27, 2009 Parametrized Gap-Hamming-Distance Problem The problem:  if ∆( x, y ) ≥ n/ 2 + c √ n ,  1 ,   if ∆( x, y ) ≤ n/ 2 − c √ n , ghd c,n ( x, y ) = 0 ,    � , otherwise. Hard input distribution: µ c,n : uniform over ( x, y ) such that | ∆( x, y ) − n/ 2 | ≥ c √ n Amit Chakrabarti 8-a

  19. Gap-Hamming Lower Bound March 27, 2009 Parametrized Gap-Hamming-Distance Problem The problem:  if ∆( x, y ) ≥ n/ 2 + c √ n ,  1 ,   if ∆( x, y ) ≤ n/ 2 − c √ n , ghd c,n ( x, y ) = 0 ,    � , otherwise. Hard input distribution: µ c,n : uniform over ( x, y ) such that | ∆( x, y ) − n/ 2 | ≥ c √ n Protocol assumptions (eventually, will lead to contradiction): • Deterministic k -round protocol for ghd c,n • Each message is s � n bits • Error probability ≤ ε , under distribution µ c,n Amit Chakrabarti 8-b

  20. Gap-Hamming Lower Bound March 27, 2009 Round Elimination Main Construction: Given k -round protocol P for ghd c,n , construct ( k − 1) -round protocol Q for ghd c � ,n � Amit Chakrabarti 9

  21. Gap-Hamming Lower Bound March 27, 2009 Round Elimination Main Construction: Given k -round protocol P for ghd c,n , construct ( k − 1) -round protocol Q for ghd c � ,n � First Attempt: • Fix Alice’s first message m in P , suitably Amit Chakrabarti 9-a

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