Presentation is loading. Please wait.

Presentation is loading. Please wait.

=0.01010001011.

Published byBernard Frothingham Modified over 10 years ago

Similar presentations

Presentation on theme: "=0.01010001011."— Presentation transcript:

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16 =0.01010001011

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57 RECENTLY DISCOVERED POWERFUL CLASSES OF CLASSICAL SAMPLING ALGORITHMS SOLVING HARD PROBLEMS: Ferromagnetic Ising (Jerrum, Sinclair) Permanents of non-negative matrices (Jerrum, Sinclair and Vigoda) Volumes of convex bodies in high dimensions (Dyer, Frieze, Kannan) Integration of log-concave functions (Applegate, Kannan)

58 RECENTLY DISCOVERED POWERFUL CLASSES OF CLASSICAL SAMPLING ALGORITHMS SOLVING HARD PROBLEMS: Ferromagnetic Ising (Jerrum, Sinclair) Permanents of non-negative matrices (Jerrum, Sinclair and Vigoda) Volumes of convex bodies in high dimensions (Dyer, Frieze, Kannan) Integration of log-concave functions (Applegate, Kannan)

59

60

61

Download ppt "=0.01010001011."

Similar presentations

1/15 Agnostically learning halfspaces FOCS 2005. 2/15 Set X, F class of functions f: X! {0,1}. Efficient Agnostic Learner w.h.p. h: X! {0,1} poly(1/ )

1/15 Agnostically learning halfspaces FOCS /15 Set X, F class of functions f: X! {0,1}. Efficient Agnostic Learner w.h.p. h: X! {0,1} poly(1/ )
This algorithm is used for dimension reduction. Input: a set of vectors {Xn є }, and dimension d,d

This algorithm is used for dimension reduction. Input: a set of vectors {Xn є }, and dimension d,d
Slow and Fast Mixing of Tempering and Swapping for the Potts Model Nayantara Bhatnagar, UC Berkeley Dana Randall, Georgia Tech.

Slow and Fast Mixing of Tempering and Swapping for the Potts Model Nayantara Bhatnagar, UC Berkeley Dana Randall, Georgia Tech.
New Computational Insights from Quantum Optics Scott Aaronson Based on joint work with Alex Arkhipov.

New Computational Insights from Quantum Optics Scott Aaronson Based on joint work with Alex Arkhipov.
Convex Programming Brookes Vision Reading Group. Huh? What is convex ??? What is programming ??? What is convex programming ???

Convex Programming Brookes Vision Reading Group. Huh? What is convex ??? What is programming ??? What is convex programming ???
DNF Sparsification and Counting Raghu Meka (IAS, Princeton) Parikshit Gopalan (MSR, SVC) Omer Reingold (MSR, SVC)

DNF Sparsification and Counting Raghu Meka (IAS, Princeton) Parikshit Gopalan (MSR, SVC) Omer Reingold (MSR, SVC)
Counting Algorithms for Knapsack and Related Problems 1 Raghu Meka (UT Austin, work done at MSR, SVC) Parikshit Gopalan (Microsoft Research, SVC) Adam.

Counting Algorithms for Knapsack and Related Problems 1 Raghu Meka (UT Austin, work done at MSR, SVC) Parikshit Gopalan (Microsoft Research, SVC) Adam.
Optimal Column-Based Low-Rank Matrix Reconstruction SODA’12 Ali Kemal Sinop Joint work with Prof. Venkatesan Guruswami.

Optimal Column-Based Low-Rank Matrix Reconstruction SODA’12 Ali Kemal Sinop Joint work with Prof. Venkatesan Guruswami.
Brief insight.  3.1 Understand mathematical equations appropriate to the solving of general engineering problems  3.2 Understand trigonometric functions.

Brief insight.  3.1 Understand mathematical equations appropriate to the solving of general engineering problems  3.2 Understand trigonometric functions.
Monte Carlo uniform sampling of high-dimensional convex polytopes: reducing the condition number with applications in metabolic network analysis Center.

Monte Carlo uniform sampling of high-dimensional convex polytopes: reducing the condition number with applications in metabolic network analysis Center.
Polygons – Concave and Convex Turning Point Quiz Copyright © 2010 Kelly Mott.

Polygons – Concave and Convex Turning Point Quiz Copyright © 2010 Kelly Mott.
1 Heat flow and a faster Algorithm to Compute the Surface Area of a Convex Body Hariharan Narayanan, University of Chicago Joint work with Mikhail Belkin,

1 Heat flow and a faster Algorithm to Compute the Surface Area of a Convex Body Hariharan Narayanan, University of Chicago Joint work with Mikhail Belkin,
On the monotonicity of the expected volume of a random simplex Luis Rademacher Computer Science and Engineering The Ohio State University TexPoint fonts.

On the monotonicity of the expected volume of a random simplex Luis Rademacher Computer Science and Engineering The Ohio State University TexPoint fonts.
Lesson 1.6 Classify Polygons. Objective Classify Polygons.

Lesson 1.6 Classify Polygons. Objective Classify Polygons.
10/11/2001Random walks and spectral segmentation1 CSE 291 Fall 2001 Marina Meila and Jianbo Shi: Learning Segmentation by Random Walks/A Random Walks View.

10/11/2001Random walks and spectral segmentation1 CSE 291 Fall 2001 Marina Meila and Jianbo Shi: Learning Segmentation by Random Walks/A Random Walks View.
Chris Beck, Russell Impagliazzo, Shachar Lovett. History of Sampling Problems Earliest uses of randomness in algorithms were for sampling, not decision.

Chris Beck, Russell Impagliazzo, Shachar Lovett. History of Sampling Problems Earliest uses of randomness in algorithms were for sampling, not decision.
Adaptive annealing: a near-optimal connection between sampling and counting Daniel Štefankovi č (University of Rochester) Santosh Vempala Eric Vigoda (Georgia.

Adaptive annealing: a near-optimal connection between sampling and counting Daniel Štefankovi č (University of Rochester) Santosh Vempala Eric Vigoda (Georgia.
A different view of independent sets in bipartite graphs Qi Ge Daniel Štefankovič University of Rochester.

A different view of independent sets in bipartite graphs Qi Ge Daniel Štefankovič University of Rochester.
Clustering In Large Graphs And Matrices Petros Drineas, Alan Frieze, Ravi Kannan, Santosh Vempala, V. Vinay Presented by Eric Anderson.

Clustering In Large Graphs And Matrices Petros Drineas, Alan Frieze, Ravi Kannan, Santosh Vempala, V. Vinay Presented by Eric Anderson.
Solving Exponential Equations. One-to-One Properties.

Solving Exponential Equations. One-to-One Properties.

Similar presentations

Ads by Google