t s r tt t s t t r s
play

ts r tt ts t t - PowerPoint PPT Presentation

Mar 17, 2023 •45 likes •465 views

tt tts rts rts ts ts r tt ts t

  1. ❚♦♣ ❛♥❞ ❜♦tt♦♠ ❝♦♥t❛❝ts ❱❛r✐❛t✐♦♥s ❛♥❞ ❣❡♥❡r❛❧✐③❛t✐♦♥s ❆♣♣❧✐❝❛t✐♦♥s ❇✐❥❡❝t✐♦♥s ❢♦r ❧❛tt✐❝❡ ♣❛t❤s ❜❡t✇❡❡♥ t✇♦ ❜♦✉♥❞❛r✐❡s ❙❡r❣✐ ❊❧✐③❛❧❞❡ ❉❛rt♠♦✉t❤ ❈♦❧❧❡❣❡ ❏♦✐♥t ✇♦r❦ ✇✐t❤ ▼❛rt✐♥ ❘✉❜❡② ❈❛♥❛❉❆▼ ✷✵✶✸ ❙❡r❣✐ ❊❧✐③❛❧❞❡ ❇✐❥❡❝t✐♦♥s ❢♦r ❧❛tt✐❝❡ ♣❛t❤s ❜❡t✇❡❡♥ t✇♦ ❜♦✉♥❞❛r✐❡s

  2. ❚❤❡♦r❡♠ ✭❉❡✉ts❝❤ ✬✾✽✮ ❚❤❡ ❥♦✐♥t ❞✐str✐❜✉t✐♦♥ ♦❢ t❤❡ ♣❛✐r t ❜ ♦✈❡r ♥ ✐s s②♠♠❡tr✐❝✱ ✐✳❡✳✱ ① t P ② ❜ P ① ❜ P ② t P P P ♥ ♥ Pr♦♦❢ ✶ ✭❉❡✉ts❝❤✮✿ ❘❡❝✉rs✐✈❡ ❜✐❥❡❝t✐♦♥✳ Pr♦♦❢ ✷✿ ●❡♥❡r❛t✐♥❣ ❢❝ts✳ ❇♦t❤ ♣r♦♦❢s r❡❧② ♦♥ t❤❡ r❡❝✉rs✐✈❡ str✉❝t✉r❡ ♦❢ ❉②❝❦ ♣❛t❤s✳ ❚♦♣ ❛♥❞ ❜♦tt♦♠ ❝♦♥t❛❝ts P❛t❤s ✇✐t❤ st❡♣s ◆ , ❊ ❱❛r✐❛t✐♦♥s ❛♥❞ ❣❡♥❡r❛❧✐③❛t✐♦♥s ❚❤❡ ❜✐❥❡❝t✐♦♥ ❆♣♣❧✐❝❛t✐♦♥s ❉②❝❦ ♣❛t❤s ❚ ❋♦r P ∈ D ♥ ✭❉②❝❦ ♣❛t❤s ✇✐t❤ ✷ ♥ st❡♣s✮✱ ❧❡t P ❇ t ( P ) = # ♦❢ ❊ st❡♣s ✐♥ ❝♦♠♠♦♥ ✇✐t❤ ❚ = ✏❤❡✐❣❤t✑ ♦❢ t❤❡ ❧❛st ✏♣❡❛❦✑ ❜ ( P ) = # ♦❢ ❊ st❡♣s ✐♥ ❝♦♠♠♦♥ ✇✐t❤ ❇ = ♥✉♠❜❡r ♦❢ r❡t✉r♥s t ( P ) = ✸ ❜ ( P ) = ✷ ❙❡r❣✐ ❊❧✐③❛❧❞❡ ❇✐❥❡❝t✐♦♥s ❢♦r ❧❛tt✐❝❡ ♣❛t❤s ❜❡t✇❡❡♥ t✇♦ ❜♦✉♥❞❛r✐❡s

  3. Pr♦♦❢ ✶ ✭❉❡✉ts❝❤✮✿ ❘❡❝✉rs✐✈❡ ❜✐❥❡❝t✐♦♥✳ Pr♦♦❢ ✷✿ ●❡♥❡r❛t✐♥❣ ❢❝ts✳ ❇♦t❤ ♣r♦♦❢s r❡❧② ♦♥ t❤❡ r❡❝✉rs✐✈❡ str✉❝t✉r❡ ♦❢ ❉②❝❦ ♣❛t❤s✳ ❚♦♣ ❛♥❞ ❜♦tt♦♠ ❝♦♥t❛❝ts P❛t❤s ✇✐t❤ st❡♣s ◆ , ❊ ❱❛r✐❛t✐♦♥s ❛♥❞ ❣❡♥❡r❛❧✐③❛t✐♦♥s ❚❤❡ ❜✐❥❡❝t✐♦♥ ❆♣♣❧✐❝❛t✐♦♥s ❉②❝❦ ♣❛t❤s ❚ ❋♦r P ∈ D ♥ ✭❉②❝❦ ♣❛t❤s ✇✐t❤ ✷ ♥ st❡♣s✮✱ ❧❡t P ❇ t ( P ) = # ♦❢ ❊ st❡♣s ✐♥ ❝♦♠♠♦♥ ✇✐t❤ ❚ = ✏❤❡✐❣❤t✑ ♦❢ t❤❡ ❧❛st ✏♣❡❛❦✑ ❜ ( P ) = # ♦❢ ❊ st❡♣s ✐♥ ❝♦♠♠♦♥ ✇✐t❤ ❇ = ♥✉♠❜❡r ♦❢ r❡t✉r♥s t ( P ) = ✸ ❜ ( P ) = ✷ ❚❤❡♦r❡♠ ✭❉❡✉ts❝❤ ✬✾✽✮ ❚❤❡ ❥♦✐♥t ❞✐str✐❜✉t✐♦♥ ♦❢ t❤❡ ♣❛✐r ( t , ❜ ) ♦✈❡r D ♥ ✐s s②♠♠❡tr✐❝✱ ✐✳❡✳✱ � � ① t ( P ) ② ❜ ( P ) = ① ❜ ( P ) ② t ( P ) . P ∈D ♥ P ∈D ♥ ❙❡r❣✐ ❊❧✐③❛❧❞❡ ❇✐❥❡❝t✐♦♥s ❢♦r ❧❛tt✐❝❡ ♣❛t❤s ❜❡t✇❡❡♥ t✇♦ ❜♦✉♥❞❛r✐❡s

  4. ❚♦♣ ❛♥❞ ❜♦tt♦♠ ❝♦♥t❛❝ts P❛t❤s ✇✐t❤ st❡♣s ◆ , ❊ ❱❛r✐❛t✐♦♥s ❛♥❞ ❣❡♥❡r❛❧✐③❛t✐♦♥s ❚❤❡ ❜✐❥❡❝t✐♦♥ ❆♣♣❧✐❝❛t✐♦♥s ❉②❝❦ ♣❛t❤s ❚ ❋♦r P ∈ D ♥ ✭❉②❝❦ ♣❛t❤s ✇✐t❤ ✷ ♥ st❡♣s✮✱ ❧❡t P ❇ t ( P ) = # ♦❢ ❊ st❡♣s ✐♥ ❝♦♠♠♦♥ ✇✐t❤ ❚ = ✏❤❡✐❣❤t✑ ♦❢ t❤❡ ❧❛st ✏♣❡❛❦✑ ❜ ( P ) = # ♦❢ ❊ st❡♣s ✐♥ ❝♦♠♠♦♥ ✇✐t❤ ❇ = ♥✉♠❜❡r ♦❢ r❡t✉r♥s t ( P ) = ✸ ❜ ( P ) = ✷ ❚❤❡♦r❡♠ ✭❉❡✉ts❝❤ ✬✾✽✮ ❚❤❡ ❥♦✐♥t ❞✐str✐❜✉t✐♦♥ ♦❢ t❤❡ ♣❛✐r ( t , ❜ ) ♦✈❡r D ♥ ✐s s②♠♠❡tr✐❝✱ ✐✳❡✳✱ � � ① t ( P ) ② ❜ ( P ) = ① ❜ ( P ) ② t ( P ) . P ∈D ♥ P ∈D ♥ Pr♦♦❢ ✶ ✭❉❡✉ts❝❤✮✿ ❘❡❝✉rs✐✈❡ ❜✐❥❡❝t✐♦♥✳ Pr♦♦❢ ✷✿ ●❡♥❡r❛t✐♥❣ ❢❝ts✳ ❇♦t❤ ♣r♦♦❢s r❡❧② ♦♥ t❤❡ r❡❝✉rs✐✈❡ str✉❝t✉r❡ ♦❢ ❉②❝❦ ♣❛t❤s✳ ❙❡r❣✐ ❊❧✐③❛❧❞❡ ❇✐❥❡❝t✐♦♥s ❢♦r ❧❛tt✐❝❡ ♣❛t❤s ❜❡t✇❡❡♥ t✇♦ ❜♦✉♥❞❛r✐❡s

  5. ❚❤❡♦r❡♠ ❚❤❡ ❥♦✐♥t ❞✐str✐❜✉t✐♦♥ ♦❢ t ❜ ♦✈❡r ❚ ❇ ✐s s②♠♠❡tr✐❝✱ ✐✳❡✳✱ ① t P ② ❜ P ① ❜ P ② t P P ❚ ❇ P ❚ ❇ ❚♦♣ ❛♥❞ ❜♦tt♦♠ ❝♦♥t❛❝ts P❛t❤s ✇✐t❤ st❡♣s ◆ , ❊ ❱❛r✐❛t✐♦♥s ❛♥❞ ❣❡♥❡r❛❧✐③❛t✐♦♥s ❚❤❡ ❜✐❥❡❝t✐♦♥ ❆♣♣❧✐❝❛t✐♦♥s ❆ ❣❡♥❡r❛❧✐③❛t✐♦♥ t♦ ❛r❜✐tr❛r② ❜♦✉♥❞❛r✐❡s ❚ ❛♥❞ ❇ ♣❛t❤s ❢r♦♠ ❖ t♦ ❋ ✇✐t❤ st❡♣s ❋ ◆ ❛♥❞ ❊ ✱ ✇✐t❤ ❚ ✇❡❛❦❧② ❛❜♦✈❡ ❇ ❚ P ∈ P ( ❚ , ❇ ) = s❡t ♦❢ ♣❛t❤s ❢r♦♠ ❖ t♦ ❋ ✇❡❛❦❧② ❜❡t✇❡❡♥ ❚ ❛♥❞ ❇ t ( P ) =# ♦❢ ❊ st❡♣s ✐♥ ❝♦♠♠♦♥ ✇✐t❤ ❚ ❇ P ✭t♦♣ ❝♦♥t❛❝ts ♦❢ P ✮ ❖ ❜ ( P ) =# ♦❢ ❊ st❡♣s ✐♥ ❝♦♠♠♦♥ ✇✐t❤ ❇ t ( P ) = ✹ ❜ ( P ) = ✸ ✭❜♦tt♦♠ ❝♦♥t❛❝ts ♦❢ P ✮ ❙❡r❣✐ ❊❧✐③❛❧❞❡ ❇✐❥❡❝t✐♦♥s ❢♦r ❧❛tt✐❝❡ ♣❛t❤s ❜❡t✇❡❡♥ t✇♦ ❜♦✉♥❞❛r✐❡s

  6. ❚♦♣ ❛♥❞ ❜♦tt♦♠ ❝♦♥t❛❝ts P❛t❤s ✇✐t❤ st❡♣s ◆ , ❊ ❱❛r✐❛t✐♦♥s ❛♥❞ ❣❡♥❡r❛❧✐③❛t✐♦♥s ❚❤❡ ❜✐❥❡❝t✐♦♥ ❆♣♣❧✐❝❛t✐♦♥s ❆ ❣❡♥❡r❛❧✐③❛t✐♦♥ t♦ ❛r❜✐tr❛r② ❜♦✉♥❞❛r✐❡s ❚ ❛♥❞ ❇ ♣❛t❤s ❢r♦♠ ❖ t♦ ❋ ✇✐t❤ st❡♣s ❋ ◆ ❛♥❞ ❊ ✱ ✇✐t❤ ❚ ✇❡❛❦❧② ❛❜♦✈❡ ❇ ❚ P ∈ P ( ❚ , ❇ ) = s❡t ♦❢ ♣❛t❤s ❢r♦♠ ❖ t♦ ❋ ✇❡❛❦❧② ❜❡t✇❡❡♥ ❚ ❛♥❞ ❇ t ( P ) =# ♦❢ ❊ st❡♣s ✐♥ ❝♦♠♠♦♥ ✇✐t❤ ❚ ❇ P ✭t♦♣ ❝♦♥t❛❝ts ♦❢ P ✮ ❖ ❜ ( P ) =# ♦❢ ❊ st❡♣s ✐♥ ❝♦♠♠♦♥ ✇✐t❤ ❇ t ( P ) = ✹ ❜ ( P ) = ✸ ✭❜♦tt♦♠ ❝♦♥t❛❝ts ♦❢ P ✮ ❚❤❡♦r❡♠ ❚❤❡ ❥♦✐♥t ❞✐str✐❜✉t✐♦♥ ♦❢ ( t , ❜ ) ♦✈❡r P ( ❚ , ❇ ) ✐s s②♠♠❡tr✐❝✱ ✐✳❡✳✱ � � ① t ( P ) ② ❜ ( P ) = ① ❜ ( P ) ② t ( P ) . P ∈P ( ❚ , ❇ ) P ∈P ( ❚ , ❇ ) ❙❡r❣✐ ❊❧✐③❛❧❞❡ ❇✐❥❡❝t✐♦♥s ❢♦r ❧❛tt✐❝❡ ♣❛t❤s ❜❡t✇❡❡♥ t✇♦ ❜♦✉♥❞❛r✐❡s

  7. ❚♦♣ ❛♥❞ ❜♦tt♦♠ ❝♦♥t❛❝ts P❛t❤s ✇✐t❤ st❡♣s ◆ , ❊ ❱❛r✐❛t✐♦♥s ❛♥❞ ❣❡♥❡r❛❧✐③❛t✐♦♥s ❚❤❡ ❜✐❥❡❝t✐♦♥ ❆♣♣❧✐❝❛t✐♦♥s ❊①❛♠♣❧❡ ① ✸ ① ✷ ① ① ✷ ① ✶ ② ✷ ①② ② ① ✷ ② ①② ② ①② ✷ ② ✷ ② ✸ ① t ( P ) ② ❜ ( P ) = ① ✸ + ① ✷ ② + ①② ✷ + ② ✸ + ✷ ① ✷ + ✷ ①② + ✷ ② ✷ + ✷ ① + ✷ ② + ✶ � P ∈P ( ❚ , ❇ ) ❙❡r❣✐ ❊❧✐③❛❧❞❡ ❇✐❥❡❝t✐♦♥s ❢♦r ❧❛tt✐❝❡ ♣❛t❤s ❜❡t✇❡❡♥ t✇♦ ❜♦✉♥❞❛r✐❡s

  8. ❲❡ ❣✐✈❡ ❛♥ ✐♥✈♦❧✉t✐♦♥ ❚ ❇ ❚ ❇ ✇✐t❤ t❤❡ ♣r♦♣❡rt② t P ❜ P ❛♥❞ ❜ P t P ✳ ■❞❡❛✿ ●✐✈❡♥ P ❚ ❇ ✇✐t❤ t P ❜ P ✱ t✉r♥ s♦♠❡ ♦❢ ✐ts t♦♣ ❝♦♥t❛❝ts ✐♥t♦ ❜♦tt♦♠ ❝♦♥t❛❝ts✱ ♦♥❡ ❛t ❛ t✐♠❡✳ ❚♦♣ ❛♥❞ ❜♦tt♦♠ ❝♦♥t❛❝ts P❛t❤s ✇✐t❤ st❡♣s ◆ , ❊ ❱❛r✐❛t✐♦♥s ❛♥❞ ❣❡♥❡r❛❧✐③❛t✐♦♥s ❚❤❡ ❜✐❥❡❝t✐♦♥ ❆♣♣❧✐❝❛t✐♦♥s Pr♦♦❢ ❚❤❡ ❦♥♦✇♥ ♣r♦♦❢s ❢♦r ❉②❝❦ ♣❛t❤s ❞♦ ♥♦t s❡❡♠ t♦ ❣❡♥❡r❛❧✐③❡ t♦ ❛r❜✐tr❛r② ❜♦✉♥❞❛r✐❡s✳ ❙❡r❣✐ ❊❧✐③❛❧❞❡ ❇✐❥❡❝t✐♦♥s ❢♦r ❧❛tt✐❝❡ ♣❛t❤s ❜❡t✇❡❡♥ t✇♦ ❜♦✉♥❞❛r✐❡s

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

Dead Virtual Function Elimination (VFE) in LLVM An example wed like to optimise struct A {

Dead Virtual Function Elimination (VFE) in LLVM An example wed like to optimise struct A {

Dead Virtual Function Elimination (VFE) in LLVM An example wed like to optimise struct A { virtual int foo() { return 1; } virtual int bar() { return 2; } }; struct B : A { virtual int foo() { return 3; } virtual int bar() { return 4; }

354 views • 12 slides
4.2. Hotelling Model The model: 1. Linear city is the interval [0,1] 2. Consumers are

4.2. Hotelling Model The model: 1. Linear city is the interval [0,1] 2. Consumers are

4.2. Hotelling Model Matilde Machado 4.2. Hotelling Model The

411 views • 18 slides
Distal NIP theories P. Simon NIP Theories Definition A formula ( x ; y ) has the independence

Distal NIP theories P. Simon NIP Theories Definition A formula ( x ; y ) has the independence

Distal NIP theories P. Simon NIP Theories Definition A formula ( x ; y ) has the independence property if one can find some infinite set B such that for every C B , there is y C such that for x B , ( x ; y C ) x C . A

389 views • 22 slides
5. Duality Lagrange dual problem weak and strong duality geometric interpretation

5. Duality Lagrange dual problem weak and strong duality geometric interpretation

Convex Optimization Boyd & Vandenberghe 5. Duality Lagrange dual problem weak and strong duality geometric interpretation optimality conditions perturbation and sensitivity analysis examples generalized

642 views • 30 slides
JUST THE MATHS SLIDES NUMBER 1.4 ALGEBRA 4 (Logarithms) by A.J.Hobson 1.4.1 Common

JUST THE MATHS SLIDES NUMBER 1.4 ALGEBRA 4 (Logarithms) by A.J.Hobson 1.4.1 Common

JUST THE MATHS SLIDES NUMBER 1.4 ALGEBRA 4 (Logarithms) by A.J.Hobson 1.4.1 Common logarithms 1.4.2 Logarithms in general 1.4.3 Useful Results 1.4.4 Properties of logarithms 1.4.5 Natural logarithms 1.4.6 Graphs of logarithmic and

397 views • 13 slides
Stakeholders Meeting Training for Housing Counselors The audio is available only through the

Stakeholders Meeting Training for Housing Counselors The audio is available only through the

Stakeholders Meeting Training for Housing Counselors The audio is available only through the conference call. Please call: (800) 700-7784 Participant Access Code: 368551 to join the conference call portion of the webinar. 10/20/2015 1 Webinar

435 views • 40 slides
Flavor Physics and Dark Matter in SUSY GUT Models Yudi Santoso Based on works with Bhaskar Dutta

Flavor Physics and Dark Matter in SUSY GUT Models Yudi Santoso Based on works with Bhaskar Dutta

Flavor Physics and Dark Matter in SUSY GUT Models Yudi Santoso Based on works with Bhaskar Dutta & Yukihiro Mimura PRD80:095005,2009 PHENO 2010, Madison, 11 th May 2010 1 Supersymmetry Why SUSY? It provides solution to hierarchy problem,

369 views • 13 slides
r st Ps P

r st Ps P

r st Ps P st rtr ts s P rtPr

702 views • 42 slides
Macro II/Aussenwirtschaft Lecture Slides No 6 Gerald Willmann May 2020 c Gerald Willmann,

Macro II/Aussenwirtschaft Lecture Slides No 6 Gerald Willmann May 2020 c Gerald Willmann,

Macro II/Aussenwirtschaft Lecture Slides No 6 Gerald Willmann May 2020 c Gerald Willmann, Bielefeld University Trade Policy lets start by considering an import tariff thats a tax on imports revenue collected by the state

353 views • 16 slides
Derivation reduction of metarules in meta-interpretive learning Andrew Cropper & Sophie

Derivation reduction of metarules in meta-interpretive learning Andrew Cropper & Sophie

Derivation reduction of metarules in meta-interpretive learning Andrew Cropper & Sophie Tourret Input Output Examples Background knowledge Logic program Bias Biases - Mode declarations (Progol, ILASP, Aleph, XHAIL, ) - Metarules

839 views • 51 slides
Oriented Paramodulation 17ai We can use the idea of ordering an equation to control

Oriented Paramodulation 17ai We can use the idea of ordering an equation to control

Oriented Paramodulation 17ai We can use the idea of ordering an equation to control paramodulation steps: Restrict paramodulation by requiring the replacing term to be definitely not greater than the one being replaced. AUTOMATED

180 views • 4 slides
Trusting Trusting the Cloud with the Cloud with Practical Interact Practical Interactive ive

Trusting Trusting the Cloud with the Cloud with Practical Interact Practical Interactive ive

Trusting Trusting the Cloud with the Cloud with Practical Interact Practical Interactive ive Proofs Proofs Graham Cormode G.Cormode@warwick.ac.uk Amit Chakrabarti (Dartmouth) Andrew McGregor (U Mass Amherst) Justin Thaler (Harvard/Yahoo!)

257 views • 14 slides
Section 7: Ring theory Matthew Macauley Department of Mathematical Sciences Clemson University

Section 7: Ring theory Matthew Macauley Department of Mathematical Sciences Clemson University

Section 7: Ring theory Matthew Macauley Department of Mathematical Sciences Clemson University http://www.math.clemson.edu/~macaule/ Math 4120, Modern Algebra M. Macauley (Clemson) Section 7: Ring theory Math 4120, Modern algebra 1 / 46

1.42k views • 46 slides
DISCRETE LOGARITHMS Elliptic Curve Cryptography December 2019 Bochum, Germany IN

DISCRETE LOGARITHMS Elliptic Curve Cryptography December 2019 Bochum, Germany IN

ECC 2019: 23rd Workshop on DISCRETE LOGARITHMS Elliptic Curve Cryptography December 2019 Bochum, Germany IN QUASI-POLYNOMIAL TIME Based on a joint work with Thorsten Kleinjung IN FINITE FIELDS OF SMALL CHARACTERISTIC Benjamin Wesolowski

700 views • 47 slides
Slide 1 / 42 Slide 2 / 42 1. Block 1 with a mass of 500 g moves at a constant speed of 5 m/s on

Slide 1 / 42 Slide 2 / 42 1. Block 1 with a mass of 500 g moves at a constant speed of 5 m/s on

Slide 1 / 42 Slide 2 / 42 1. Block 1 with a mass of 500 g moves at a constant speed of 5 m/s on a horizontal frictionless track and collides and sticks to a stationary block 2 mass of 1.5 kg. Block 2 is attached to an unstretched spring with a

338 views • 7 slides
Probability review INFO 1301 Prof. Michael Paul Prof. William Aspray R1

Probability review INFO 1301 Prof. Michael Paul Prof. William Aspray R1

Probability review INFO 1301 Prof. Michael Paul Prof. William Aspray R1 The probability of an outcome is the proportion of times the outcome would occur if we observed the random process an infinite number of times.

335 views • 8 slides
Change of Basis Marco Chiarandini Department of Mathematics & Computer Science University of

Change of Basis Marco Chiarandini Department of Mathematics & Computer Science University of

DM559 Linear and Integer Programming Lecture 8 Change of Basis Marco Chiarandini Department of Mathematics & Computer Science University of Southern Denmark Coordinate Change Outline 1. Coordinate Change 2 Coordinate Change Resume

504 views • 14 slides
Flexibly Fair Representation Learning by Disentanglement Elliot Creager 1 2 David Madras 1 2 J

Flexibly Fair Representation Learning by Disentanglement Elliot Creager 1 2 David Madras 1 2 J

Flexibly Fair Representation Learning by Disentanglement Elliot Creager 1 2 David Madras 1 2 J orn-Henrik Jacobsen 2 Marissa A. Weis 2 3 Kevin Swersky 4 Toniann Pitassi 1 2 Richard Zemel 1 2 June 13, 2019 1 University of Toronto 2 Vector

533 views • 8 slides
Big picture All languages Decidable Turing machines NP P Context-free

Big picture All languages Decidable Turing machines NP P Context-free

Big picture All languages Decidable Turing machines NP P Context-free Context-free grammars, push-down automata Regular Automata, non-deterministic automata, regular expressions Recall: Theorem: L := {0 n 1 n : n 0}

1.23k views • 108 slides
Hopf orders in Hopf algebras with trivial Verschiebung Alan Koch Agnes Scott College June, 2015

Hopf orders in Hopf algebras with trivial Verschiebung Alan Koch Agnes Scott College June, 2015

Hopf orders in Hopf algebras with trivial Verschiebung Alan Koch Agnes Scott College June, 2015 Alan Koch (Agnes Scott College) 1 / 47 Overview, Assumptions Let R be a complete discrete valuation ring of (equal) characteristic p , K = Frac R

677 views • 48 slides
Continuous Representations: What goes right and what goes wrong? Supplementary Slides Job Rock

Continuous Representations: What goes right and what goes wrong? Supplementary Slides Job Rock

Continuous Representations: What goes right and what goes wrong? Supplementary Slides Job Rock Department of Mathematics Brandeis University Joint with Kiyoshi Igusa and Gordana Todorov Representation Theory and Related Topics Seminar 3 May

325 views • 29 slides
On some open questions related to transport equations with critical regularity F. Bouchut 1 1

On some open questions related to transport equations with critical regularity F. Bouchut 1 1

On some open questions related to transport equations with critical regularity F. Bouchut 1 1 LAMA, CNRS & Universit e Paris-Est-Marne-la-Vall ee Basel, June 2017 Transport equations with critical regularity 1 Outline 1 A review of

757 views • 31 slides
Limits on the Hardness of Lattice Problems in p Norms Chris Peikert SRI International

Limits on the Hardness of Lattice Problems in p Norms Chris Peikert SRI International

Limits on the Hardness of Lattice Problems in p Norms Chris Peikert SRI International Complexity 2007 1 / 12 Lattices and Their Problems Let B = { b 1 , . . . , b n } R n be linearly independent. The n -dim lattice L having basis B is:

902 views • 58 slides
Algorithms (2IL15) Lecture 10 NP-Completeness, II 1 TU/e Algorithms (2IL15) Lecture 10

Algorithms (2IL15) Lecture 10 NP-Completeness, II 1 TU/e Algorithms (2IL15) Lecture 10

TU/e Algorithms (2IL15) Lecture 10 Algorithms (2IL15) Lecture 10 NP-Completeness, II 1 TU/e Algorithms (2IL15) Lecture 10 Summary of previous lecture P: class of decision problems that can be solved in polynomial time NP:

496 views • 34 slides

More recommend