Can linear programs solve NP-hard problems?

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1 Can linear programs solve NP-hard problems? p. 1/9 Can linear programs solve NP-hard problems? Ronald de Wolf

2 Linear programs Can linear programs solve NP-hard problems? p. 2/9

3 Can linear programs solve NP-hard problems? p. 2/9 Linear programs A factory produces 2 types of goods

4 Can linear programs solve NP-hard problems? p. 2/9 Linear programs A factory produces 2 types of goods - unit of type 1 gives profite1; type 2 gives profite4

5 Can linear programs solve NP-hard problems? p. 2/9 Linear programs A factory produces 2 types of goods - unit of type 1 gives profite1; type 2 gives profite4 It has 4 kinds of resources: 650 of R 1, 100 of R 2,...

6 Can linear programs solve NP-hard problems? p. 2/9 Linear programs A factory produces 2 types of goods - unit of type 1 gives profite1; type 2 gives profite4 It has 4 kinds of resources: 650 of R 1, 100 of R 2,... - type 1 uses 34 units of R 1, type 2 uses 16 units of R 1

7 Can linear programs solve NP-hard problems? p. 2/9 Linear programs A factory produces 2 types of goods - unit of type 1 gives profite1; type 2 gives profite4 It has 4 kinds of resources: 650 of R 1, 100 of R 2,... - type 1 uses 34 units of R 1, type 2 uses 16 units of R 1 Maximizing profit is linear program

8 Can linear programs solve NP-hard problems? p. 2/9 Linear programs A factory produces 2 types of goods - unit of type 1 gives profite1; type 2 gives profite4 It has 4 kinds of resources: 650 of R 1, 100 of R 2,... - type 1 uses 34 units of R 1, type 2 uses 16 units of R 1 Maximizing profit is linear program: max x 1 +4x 2 s.t. 34x 1 +16x x 1,x 2 0

9 Can linear programs solve NP-hard problems? p. 2/9 Linear programs A factory produces 2 types of goods - unit of type 1 gives profite1; type 2 gives profite4 It has 4 kinds of resources: 650 of R 1, 100 of R 2,... - type 1 uses 34 units of R 1, type 2 uses 16 units of R 1 Maximizing profit is linear program: max x 1 +4x 2 Feasible region is a polytope s.t. 34x 1 +16x x 1,x 2 0

10 A bit of history of LPs Can linear programs solve NP-hard problems? p. 3/9

11 Can linear programs solve NP-hard problems? p. 3/9 A bit of history of LPs Simplex algorithm: developed by Kantorovich and Dantzig for use in WWII, published by Dantzig in 1947.

12 Can linear programs solve NP-hard problems? p. 3/9 A bit of history of LPs Simplex algorithm: developed by Kantorovich and Dantzig for use in WWII, published by Dantzig in Walks along vertices of polytope, optimizing objective

13 Can linear programs solve NP-hard problems? p. 3/9 A bit of history of LPs Simplex algorithm: developed by Kantorovich and Dantzig for use in WWII, published by Dantzig in Walks along vertices of polytope, optimizing objective Usually efficient in practice, worst-case exponential time

14 Can linear programs solve NP-hard problems? p. 3/9 A bit of history of LPs Simplex algorithm: developed by Kantorovich and Dantzig for use in WWII, published by Dantzig in Walks along vertices of polytope, optimizing objective Usually efficient in practice, worst-case exponential time Ellipsoid method (Khachiyan 79): takes polynomial time in the worst case, but is not practical

15 Can linear programs solve NP-hard problems? p. 3/9 A bit of history of LPs Simplex algorithm: developed by Kantorovich and Dantzig for use in WWII, published by Dantzig in Walks along vertices of polytope, optimizing objective Usually efficient in practice, worst-case exponential time Ellipsoid method (Khachiyan 79): takes polynomial time in the worst case, but is not practical Interior point method (Karmarkar 84): reasonably efficient in practice

16 Can we solve NP-hard problems by LP? Can linear programs solve NP-hard problems? p. 4/9

17 Can linear programs solve NP-hard problems? p. 4/9 Can we solve NP-hard problems by LP? Famous NP-hard problem: traveling salesman problem Find shortest cycle that goes through each of n cities exactly once

18 Can linear programs solve NP-hard problems? p. 4/9 Can we solve NP-hard problems by LP? Famous NP-hard problem: traveling salesman problem Find shortest cycle that goes through each of n cities exactly once A polynomial-size LP for TSP would show P = NP

19 Can linear programs solve NP-hard problems? p. 4/9 Can we solve NP-hard problems by LP? Famous NP-hard problem: traveling salesman problem Find shortest cycle that goes through each of n cities exactly once A polynomial-size LP for TSP would show P = NP Swart claimed to have found such LPs

20 Can linear programs solve NP-hard problems? p. 4/9 Can we solve NP-hard problems by LP? Famous NP-hard problem: traveling salesman problem Find shortest cycle that goes through each of n cities exactly once A polynomial-size LP for TSP would show P = NP Swart claimed to have found such LPs Yannakakis 88: symmetric LPs for TSP are exponential

21 Can linear programs solve NP-hard problems? p. 4/9 Can we solve NP-hard problems by LP? Famous NP-hard problem: traveling salesman problem Find shortest cycle that goes through each of n cities exactly once A polynomial-size LP for TSP would show P = NP Swart claimed to have found such LPs Yannakakis 88: symmetric LPs for TSP are exponential Swart s LPs were symmetric, so they couldn t work

22 Can linear programs solve NP-hard problems? p. 4/9 Can we solve NP-hard problems by LP? Famous NP-hard problem: traveling salesman problem Find shortest cycle that goes through each of n cities exactly once A polynomial-size LP for TSP would show P = NP Swart claimed to have found such LPs Yannakakis 88: symmetric LPs for TSP are exponential Swart s LPs were symmetric, so they couldn t work 20-year open problem: what about non-symmetric LP?

23 Can linear programs solve NP-hard problems? p. 4/9 Can we solve NP-hard problems by LP? Famous NP-hard problem: traveling salesman problem Find shortest cycle that goes through each of n cities exactly once A polynomial-size LP for TSP would show P = NP Swart claimed to have found such LPs Yannakakis 88: symmetric LPs for TSP are exponential Swart s LPs were symmetric, so they couldn t work 20-year open problem: what about non-symmetric LP? Sometimes non-symmetry helps a lot! (Kaibel et al 10)

24 Can linear programs solve NP-hard problems? p. 4/9 Can we solve NP-hard problems by LP? Famous NP-hard problem: traveling salesman problem Find shortest cycle that goes through each of n cities exactly once A polynomial-size LP for TSP would show P = NP Swart claimed to have found such LPs Yannakakis 88: symmetric LPs for TSP are exponential Swart s LPs were symmetric, so they couldn t work 20-year open problem: what about non-symmetric LP? Sometimes non-symmetry helps a lot! (Kaibel et al 10) Fiorini, Massar, Pokutta, Tiwary, dw (STOC 12): any LP for TSP needs exponential size

25 Polytopes and optimization problems Can linear programs solve NP-hard problems? p. 5/9

26 Can linear programs solve NP-hard problems? p. 5/9 Polytopes and optimization problems Polytope P: convex hull of finite set of points in R d

27 Can linear programs solve NP-hard problems? p. 5/9 Polytopes and optimization problems Polytope P: convex hull of finite set of points in R d bounded intersection of finitely many halfspaces

28 Can linear programs solve NP-hard problems? p. 5/9 Polytopes and optimization problems Polytope P: convex hull of finite set of points in R d bounded intersection of finitely many halfspaces Can be written as system of linear inequalities: P = {x R d Ax b}

29 Can linear programs solve NP-hard problems? p. 5/9 Polytopes and optimization problems Polytope P: convex hull of finite set of points in R d bounded intersection of finitely many halfspaces Can be written as system of linear inequalities: P = {x R d Ax b} Different systems Ax b can define the same P

30 Can linear programs solve NP-hard problems? p. 5/9 Polytopes and optimization problems Polytope P: convex hull of finite set of points in R d bounded intersection of finitely many halfspaces Can be written as system of linear inequalities: P = {x R d Ax b} Different systems Ax b can define the same P The size of P is the minimal number of inequalities

31 Can linear programs solve NP-hard problems? p. 5/9 Polytopes and optimization problems Polytope P: convex hull of finite set of points in R d bounded intersection of finitely many halfspaces Can be written as system of linear inequalities: P = {x R d Ax b} Different systems Ax b can define the same P The size of P is the minimal number of inequalities TSP polytope: each cycle in K n is vector {0,1} (n 2)

32 Can linear programs solve NP-hard problems? p. 5/9 Polytopes and optimization problems Polytope P: convex hull of finite set of points in R d bounded intersection of finitely many halfspaces Can be written as system of linear inequalities: P = {x R d Ax b} Different systems Ax b can define the same P The size of P is the minimal number of inequalities TSP polytope: each cycle in K n is vector {0,1} (n 2). TSP(n) is the convex hull of all those vectors

33 Can linear programs solve NP-hard problems? p. 5/9 Polytopes and optimization problems Polytope P: convex hull of finite set of points in R d bounded intersection of finitely many halfspaces Can be written as system of linear inequalities: P = {x R d Ax b} Different systems Ax b can define the same P The size of P is the minimal number of inequalities TSP polytope: each cycle in K n is vector {0,1} (n 2). TSP(n) is the convex hull of all those vectors Solving TSP w.r.t. weight function w ij : minimize the linear function i,j w ijx ij over x TSP(n)

34 Can linear programs solve NP-hard problems? p. 5/9 Polytopes and optimization problems Polytope P: convex hull of finite set of points in R d bounded intersection of finitely many halfspaces Can be written as system of linear inequalities: P = {x R d Ax b} Different systems Ax b can define the same P The size of P is the minimal number of inequalities TSP polytope: each cycle in K n is vector {0,1} (n 2). TSP(n) is the convex hull of all those vectors Solving TSP w.r.t. weight function w ij : minimize the linear function i,j w ijx ij over x TSP(n) TSP(n) has exponential size

35 Extended formulations of polytopes Can linear programs solve NP-hard problems? p. 6/9

36 Can linear programs solve NP-hard problems? p. 6/9 Extended formulations of polytopes Sometimes extra variables/dimensions can reduce size very much.

37 Can linear programs solve NP-hard problems? p. 6/9 Extended formulations of polytopes Sometimes extra variables/dimensions can reduce size very much. Regular n-gon in R 2 has size n, but is the projection of polytope Q in higher dimension, of size O(logn)

38 Can linear programs solve NP-hard problems? p. 6/9 Extended formulations of polytopes Sometimes extra variables/dimensions can reduce size very much. Regular n-gon in R 2 has size n, but is the projection of polytope Q in higher dimension, of size O(logn) Optimizing over P reduces to optimizing over Q. If Q has small size, this can be done efficiently!

39 Can linear programs solve NP-hard problems? p. 6/9 Extended formulations of polytopes Sometimes extra variables/dimensions can reduce size very much. Regular n-gon in R 2 has size n, but is the projection of polytope Q in higher dimension, of size O(logn) Optimizing over P reduces to optimizing over Q. If Q has small size, this can be done efficiently! How small can size(q) be?

40 Can linear programs solve NP-hard problems? p. 6/9 Extended formulations of polytopes Sometimes extra variables/dimensions can reduce size very much. Regular n-gon in R 2 has size n, but is the projection of polytope Q in higher dimension, of size O(logn) Optimizing over P reduces to optimizing over Q. If Q has small size, this can be done efficiently! How small can size(q) be? Extension complexity: xc(p) = min{size(q) Q projects to P}

41 Can linear programs solve NP-hard problems? p. 6/9 Extended formulations of polytopes Sometimes extra variables/dimensions can reduce size very much. Regular n-gon in R 2 has size n, but is the projection of polytope Q in higher dimension, of size O(logn) Optimizing over P reduces to optimizing over Q. If Q has small size, this can be done efficiently! How small can size(q) be? Extension complexity: xc(p) = min{size(q) Q projects to P} Our goal: strong lower bounds on xc(p) for interesting P

42 How to bound extension complexity? Can linear programs solve NP-hard problems? p. 7/9

43 Can linear programs solve NP-hard problems? p. 7/9 How to bound extension complexity? Slack matrix S of a polytope P = conv(v) with inequalities {A i x b i } and points V = {v j }: S ij = b i A i v j

44 Can linear programs solve NP-hard problems? p. 7/9 How to bound extension complexity? Slack matrix S of a polytope P = conv(v) with inequalities {A i x b i } and points V = {v j }: S ij = b i A i v j NB: every entry is nonnegative; S is not unique

45 Can linear programs solve NP-hard problems? p. 7/9 How to bound extension complexity? Slack matrix S of a polytope P = conv(v) with inequalities {A i x b i } and points V = {v j }: S ij = b i A i v j NB: every entry is nonnegative; S is not unique Yannakakis 88: xc(p) = positive rank of S

46 Can linear programs solve NP-hard problems? p. 7/9 How to bound extension complexity? Slack matrix S of a polytope P = conv(v) with inequalities {A i x b i } and points V = {v j }: S ij = b i A i v j NB: every entry is nonnegative; S is not unique Yannakakis 88: xc(p) = positive rank of S Instead of considering all Q, can focus on slack matrix!

47 Can linear programs solve NP-hard problems? p. 7/9 How to bound extension complexity? Slack matrix S of a polytope P = conv(v) with inequalities {A i x b i } and points V = {v j }: S ij = b i A i v j NB: every entry is nonnegative; S is not unique Yannakakis 88: xc(p) = positive rank of S Instead of considering all Q, can focus on slack matrix! We can use nondeterministic communication complexity to lower bound rank + (S)

48 Can linear programs solve NP-hard problems? p. 7/9 How to bound extension complexity? Slack matrix S of a polytope P = conv(v) with inequalities {A i x b i } and points V = {v j }: S ij = b i A i v j NB: every entry is nonnegative; S is not unique Yannakakis 88: xc(p) = positive rank of S Instead of considering all Q, can focus on slack matrix! We can use nondeterministic communication complexity to lower bound rank + (S) Big problem until now: which polytope to analyze, and how to analyze its slack matrix?

49 Lower bound for correlation polytope Can linear programs solve NP-hard problems? p. 8/9

50 Can linear programs solve NP-hard problems? p. 8/9 Lower bound for correlation polytope Correlation polytope: COR(n) = conv{bb T b {0,1} n }

51 Can linear programs solve NP-hard problems? p. 8/9 Lower bound for correlation polytope Correlation polytope: COR(n) = conv{bb T b {0,1} n } Occurs naturally in NP-hard problems, e.g. MaxClique

52 Can linear programs solve NP-hard problems? p. 8/9 Lower bound for correlation polytope Correlation polytope: COR(n) = conv{bb T b {0,1} n } Occurs naturally in NP-hard problems, e.g. MaxClique We can find 2 n valid constraints (indexed by a {0,1} n ) whose slacks w.r.t. vertex bb T are M ab = (1 a T b) 2

53 Can linear programs solve NP-hard problems? p. 8/9 Lower bound for correlation polytope Correlation polytope: COR(n) = conv{bb T b {0,1} n } Occurs naturally in NP-hard problems, e.g. MaxClique We can find 2 n valid constraints (indexed by a {0,1} n ) whose slacks w.r.t. vertex bb T are M ab = (1 a T b) 2 Nondeterministic communication complexity of M was already analyzed in the quantum computing! (dw 00)

54 Can linear programs solve NP-hard problems? p. 8/9 Lower bound for correlation polytope Correlation polytope: COR(n) = conv{bb T b {0,1} n } Occurs naturally in NP-hard problems, e.g. MaxClique We can find 2 n valid constraints (indexed by a {0,1} n ) whose slacks w.r.t. vertex bb T are M ab = (1 a T b) 2 Nondeterministic communication complexity of M was already analyzed in the quantum computing! (dw 00) Take slack matrix S for COR(n), with 2 n vertices bb T for columns, 2 n a-constraints for first 2 n rows, remaining facets for other rows S =. M ab..

55 Can linear programs solve NP-hard problems? p. 8/9 Lower bound for correlation polytope Correlation polytope: COR(n) = conv{bb T b {0,1} n } Occurs naturally in NP-hard problems, e.g. MaxClique We can find 2 n valid constraints (indexed by a {0,1} n ) whose slacks w.r.t. vertex bb T are M ab = (1 a T b) 2 Nondeterministic communication complexity of M was already analyzed in the quantum computing! (dw 00) Take slack matrix S for COR(n), with 2 n vertices bb T for columns, 2 n a-constraints for first 2 n rows, remaining facets for other rows xc(cor(n)) S =. M ab..

56 Can linear programs solve NP-hard problems? p. 8/9 Lower bound for correlation polytope Correlation polytope: COR(n) = conv{bb T b {0,1} n } Occurs naturally in NP-hard problems, e.g. MaxClique We can find 2 n valid constraints (indexed by a {0,1} n ) whose slacks w.r.t. vertex bb T are M ab = (1 a T b) 2 Nondeterministic communication complexity of M was already analyzed in the quantum computing! (dw 00) Take slack matrix S for COR(n), with 2 n vertices bb T for columns, 2 n a-constraints for first 2 n rows, remaining facets for other rows xc(cor(n)) = rank + (S) S =. M ab..

57 Can linear programs solve NP-hard problems? p. 8/9 Lower bound for correlation polytope Correlation polytope: COR(n) = conv{bb T b {0,1} n } Occurs naturally in NP-hard problems, e.g. MaxClique We can find 2 n valid constraints (indexed by a {0,1} n ) whose slacks w.r.t. vertex bb T are M ab = (1 a T b) 2 Nondeterministic communication complexity of M was already analyzed in the quantum computing! (dw 00) Take slack matrix S for COR(n), with 2 n vertices bb T for columns, 2 n a-constraints for first 2 n rows, remaining facets for other rows xc(cor(n)) = rank + (S) rank + (M) S =. M ab..

58 Can linear programs solve NP-hard problems? p. 8/9 Lower bound for correlation polytope Correlation polytope: COR(n) = conv{bb T b {0,1} n } Occurs naturally in NP-hard problems, e.g. MaxClique We can find 2 n valid constraints (indexed by a {0,1} n ) whose slacks w.r.t. vertex bb T are M ab = (1 a T b) 2 Nondeterministic communication complexity of M was already analyzed in the quantum computing! (dw 00) Take slack matrix S for COR(n), with 2 n vertices bb T for columns, 2 n a-constraints for first 2 n rows, remaining facets for other rows S = xc(cor(n)) = rank + (S) rank + (M) 2 Ω(n). M ab..

59 Consequences for other polytopes Can linear programs solve NP-hard problems? p. 9/9

60 Can linear programs solve NP-hard problems? p. 9/9 Consequences for other polytopes Via reduction from the correlation polytope:

61 Can linear programs solve NP-hard problems? p. 9/9 Consequences for other polytopes Via reduction from the correlation polytope: TSP-polytope has extension complexity 2 n

62 Can linear programs solve NP-hard problems? p. 9/9 Consequences for other polytopes Via reduction from the correlation polytope: TSP-polytope has extension complexity 2 n Recently improved to 2 n by Rothvoss

63 Can linear programs solve NP-hard problems? p. 9/9 Consequences for other polytopes Via reduction from the correlation polytope: TSP-polytope has extension complexity 2 n Recently improved to 2 n by Rothvoss CUT-polytope has extension complexity 2 n

64 Can linear programs solve NP-hard problems? p. 9/9 Consequences for other polytopes Via reduction from the correlation polytope: TSP-polytope has extension complexity 2 n Recently improved to 2 n by Rothvoss CUT-polytope has extension complexity 2 n For specific graphs, the stable-set polytope has extension complexity 2 n

65 Can linear programs solve NP-hard problems? p. 9/9 Consequences for other polytopes Via reduction from the correlation polytope: TSP-polytope has extension complexity 2 n Recently improved to 2 n by Rothvoss CUT-polytope has extension complexity 2 n For specific graphs, the stable-set polytope has extension complexity 2 n So every linear program based on extended formulations needs exponentially many constraints

66 Can linear programs solve NP-hard problems? p. 9/9 Consequences for other polytopes Via reduction from the correlation polytope: TSP-polytope has extension complexity 2 n Recently improved to 2 n by Rothvoss CUT-polytope has extension complexity 2 n For specific graphs, the stable-set polytope has extension complexity 2 n So every linear program based on extended formulations needs exponentially many constraints This rules out many efficient algorithms for NP-hard problems, and refutes all P=NP proofs à la Swart

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