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Physics of Algorithms, Santa Fe 2009 Replica Symmetry and Combinatorial Optimization Johan W astlund Physics of Algorithms, Santa Fe 2009 Johan W astlund Replica Symmetry and Combinatorial Optimization Physics of Algorithms, Santa Fe

  1. Physics of Algorithms, Santa Fe 2009 Graph Exploration 2-person zero-sum game: Alice and Bob take turns choosing edges of a self-avoiding walk They pay the length of the chosen edge to the opponent, θ ≥ 0 or terminate by paying θ/ 2 to the opponent Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  2. Physics of Algorithms, Santa Fe 2009 Graph Exploration 2-person zero-sum game: Alice and Bob take turns choosing edges of a self-avoiding walk They pay the length of the chosen edge to the opponent, θ ≥ 0 or terminate by paying θ/ 2 to the opponent Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  3. Physics of Algorithms, Santa Fe 2009 Graph Exploration 2-person zero-sum game: Alice and Bob take turns choosing edges of a self-avoiding walk They pay the length of the chosen edge to the opponent, θ ≥ 0 or terminate by paying θ/ 2 to the opponent Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  4. Physics of Algorithms, Santa Fe 2009 Graph Exploration 2-person zero-sum game: Alice and Bob take turns choosing edges of a self-avoiding walk They pay the length of the chosen edge to the opponent, θ ≥ 0 or terminate by paying θ/ 2 to the opponent Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  5. Physics of Algorithms, Santa Fe 2009 Graph Exploration 2-person zero-sum game: Alice and Bob take turns choosing edges of a self-avoiding walk They pay the length of the chosen edge to the opponent, θ ≥ 0 or terminate by paying θ/ 2 to the opponent Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  6. Physics of Algorithms, Santa Fe 2009 Graph Exploration 2-person zero-sum game: Alice and Bob take turns choosing edges of a self-avoiding walk They pay the length of the chosen edge to the opponent, θ ≥ 0 or terminate by paying θ/ 2 to the opponent Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  7. Physics of Algorithms, Santa Fe 2009 Graph Exploration 2-person zero-sum game: Alice and Bob take turns choosing edges of a self-avoiding walk They pay the length of the chosen edge to the opponent, θ ≥ 0 or terminate by paying θ/ 2 to the opponent Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  8. Physics of Algorithms, Santa Fe 2009 Graph Exploration 2-person zero-sum game: Alice and Bob take turns choosing edges of a self-avoiding walk They pay the length of the chosen edge to the opponent, θ ≥ 0 or terminate by paying θ/ 2 to the opponent Edges longer than θ are irrelevant! Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  9. Physics of Algorithms, Santa Fe 2009 Diluted Matching Problem Optimization: θ ≥ 0 Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  10. Physics of Algorithms, Santa Fe 2009 Diluted Matching Problem Optimization: Partial matching θ ≥ 0 Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  11. Physics of Algorithms, Santa Fe 2009 Diluted Matching Problem Optimization: Partial matching Cost = total length of edges + θ/ 2 for each unmatched vertex θ ≥ 0 Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  12. Physics of Algorithms, Santa Fe 2009 Diluted Matching Problem Optimization: Partial matching Cost = total length of edges + θ/ 2 for each unmatched vertex θ ≥ 0 Feasible solutions exist also for odd N Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  13. Physics of Algorithms, Santa Fe 2009 Solution to Graph Exploration Fix θ and edge costs Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  14. Physics of Algorithms, Santa Fe 2009 Solution to Graph Exploration Fix θ and edge costs M ( G ) = cost of diluted matching problem Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  15. Physics of Algorithms, Santa Fe 2009 Solution to Graph Exploration Fix θ and edge costs M ( G ) = cost of diluted matching problem f ( G , v ) = Bob’s payoff under optimal play from v Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  16. Physics of Algorithms, Santa Fe 2009 Solution to Graph Exploration Fix θ and edge costs M ( G ) = cost of diluted matching problem f ( G , v ) = Bob’s payoff under optimal play from v Lemma f ( G , v ) = M ( G ) − M ( G − v ) Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  17. Physics of Algorithms, Santa Fe 2009 Solution to Graph Exploration Lemma f ( G , v ) = M ( G ) − M ( G − v ) Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  18. Physics of Algorithms, Santa Fe 2009 Solution to Graph Exploration Lemma f ( G , v ) = M ( G ) − M ( G − v ) Proof. f ( G , v ) = min( θ/ 2 , l i − f ( G − v , v i )) Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  19. Physics of Algorithms, Santa Fe 2009 Solution to Graph Exploration Lemma f ( G , v ) = M ( G ) − M ( G − v ) Proof. f ( G , v ) = min( θ/ 2 , l i − f ( G − v , v i )) M ( G ) = min( θ/ 2 + M ( G − v ) , l i + M ( G − v − v i )) Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  20. Physics of Algorithms, Santa Fe 2009 Solution to Graph Exploration Lemma f ( G , v ) = M ( G ) − M ( G − v ) Proof. f ( G , v ) = min( θ/ 2 , l i − f ( G − v , v i )) M ( G ) = min( θ/ 2 + M ( G − v ) , l i + M ( G − v − v i )) M ( G ) − M ( G − v ) = min( θ/ 2 , l i − ( M ( G − v ) − M ( G − v − v i ))) Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  21. Physics of Algorithms, Santa Fe 2009 Solution to Graph Exploration Lemma f ( G , v ) = M ( G ) − M ( G − v ) Proof. f ( G , v ) = min( θ/ 2 , l i − f ( G − v , v i )) M ( G ) = min( θ/ 2 + M ( G − v ) , l i + M ( G − v − v i )) M ( G ) − M ( G − v ) = min( θ/ 2 , l i − ( M ( G − v ) − M ( G − v − v i ))) f ( G , v ) and M ( G ) − M ( G − v ) satisfy the same recursion. Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  22. Physics of Algorithms, Santa Fe 2009 Solution to Graph Exploration Alice’s and Bob’s optimal strategies are given by the optimum diluted matchings on G and G − v respectively Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  23. Physics of Algorithms, Santa Fe 2009 PWIT-approximation Poisson Weighted Infinite Tree (Aldous) Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  24. Physics of Algorithms, Santa Fe 2009 PWIT-approximation Poisson Weighted Infinite Tree (Aldous) Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  25. Physics of Algorithms, Santa Fe 2009 PWIT-approximation Poisson Weighted Infinite Tree (Aldous) Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  26. Physics of Algorithms, Santa Fe 2009 PWIT-approximation Poisson Weighted Infinite Tree (Aldous) θ -cluster = component of the root after edges of cost more than θ have been deleted Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  27. Physics of Algorithms, Santa Fe 2009 PWIT-approximation The PWIT is a local weak limit of the mean field model: Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  28. Physics of Algorithms, Santa Fe 2009 PWIT-approximation The PWIT is a local weak limit of the mean field model: Lemma Fix positive integer k. Then there exists a coupling of the PWIT and rooted K N such that P ( isomorphic ( k , θ ) -neighborhoods ) ≥ 1 − (2 + θ ) k N 1 / 3 Has to be modified slightly for d > 1. Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  29. Physics of Algorithms, Santa Fe 2009 Graph Exploration on the PWIT Study Graph Exploration on the PWIT! Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  30. Physics of Algorithms, Santa Fe 2009 Graph Exploration on the PWIT Study Graph Exploration on the PWIT! What if the θ -cluster is infinite??? Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  31. Physics of Algorithms, Santa Fe 2009 Graph Exploration on the PWIT Study Graph Exploration on the PWIT! What if the θ -cluster is infinite??? Optimistic (Pessimistic) k -look-ahead values f k A and f k B Look k moves ahead and assume the opponent will pay θ/ 2 and terminate immediately if the game goes on beyond k moves Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  32. Physics of Algorithms, Santa Fe 2009 Graph Exploration on the PWIT Study Graph Exploration on the PWIT! What if the θ -cluster is infinite??? Optimistic (Pessimistic) k -look-ahead values f k A and f k B Look k moves ahead and assume the opponent will pay θ/ 2 and terminate immediately if the game goes on beyond k moves Infinite look-ahead values f A and f B (when k → ∞ ) Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  33. Physics of Algorithms, Santa Fe 2009 Graph Exploration on the PWIT Theorem Almost surely f A = f B Sketch of proof. Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  34. Physics of Algorithms, Santa Fe 2009 Graph Exploration on the PWIT Theorem Almost surely f A = f B Sketch of proof. Let both Alice and Bob be infinite look-ahead optimistic players Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  35. Physics of Algorithms, Santa Fe 2009 Graph Exploration on the PWIT Theorem Almost surely f A = f B Sketch of proof. Let both Alice and Bob be infinite look-ahead optimistic players If they do not agree on the value of the game, play has to go on forever Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  36. Physics of Algorithms, Santa Fe 2009 Graph Exploration on the PWIT Theorem Almost surely f A = f B Sketch of proof. Let both Alice and Bob be infinite look-ahead optimistic players If they do not agree on the value of the game, play has to go on forever Reasonable lines of play do not percolate Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  37. Physics of Algorithms, Santa Fe 2009 Graph Exploration on the PWIT Theorem Almost surely f A = f B Sketch of proof. Let both Alice and Bob be infinite look-ahead optimistic players If they do not agree on the value of the game, play has to go on forever Reasonable lines of play do not percolate Contradiction! Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  38. Physics of Algorithms, Santa Fe 2009 Replica Symmetry f A = f B means Replica Symmetry holds: To find how to match v it suffices to look at a neighborhood of size independent of N K N ✬ ✩ ✫ ✪ Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  39. Physics of Algorithms, Santa Fe 2009 Replica Symmetry f A = f B means Replica Symmetry holds: To find how to match v it suffices to look at a neighborhood of size independent of N K N ✬ ✩ q v ✫ ✪ Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  40. Physics of Algorithms, Santa Fe 2009 Replica Symmetry f A = f B means Replica Symmetry holds: To find how to match v it suffices to look at a neighborhood of size independent of N K N ✬ ✩ ✓✏ q v ✒✑ ✫ ✪ Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  41. Physics of Algorithms, Santa Fe 2009 Replica Symmetry f A = f B means Replica Symmetry holds: To find how to match v it suffices to look at a neighborhood of size independent of N K N ✬ ✩ ✓✏ q v ✒✑ ✫ ✪ Cost [Diluted Matching] p → β M ( d , θ ) − N / 2 Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  42. Physics of Algorithms, Santa Fe 2009 Results Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  43. Physics of Algorithms, Santa Fe 2009 Results Proof of convergence of Cost [Matching] N / 2 involves θ → ∞ (nontrivial) Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  44. Physics of Algorithms, Santa Fe 2009 Results Proof of convergence of Cost [Matching] N / 2 involves θ → ∞ (nontrivial) β M (1) = π 2 / 6 (Already mentioned, proved by Aldous in 2001) Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  45. Physics of Algorithms, Santa Fe 2009 Results Proof of convergence of Cost [Matching] N / 2 involves θ → ∞ (nontrivial) β M (1) = π 2 / 6 (Already mentioned, proved by Aldous in 2001) β M (2) ≈ 1 . 14351809919776 Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  46. Physics of Algorithms, Santa Fe 2009 Results Proof of convergence of Cost [Matching] N / 2 involves θ → ∞ (nontrivial) β M (1) = π 2 / 6 (Already mentioned, proved by Aldous in 2001) β M (2) ≈ 1 . 14351809919776 β TSP (2) ≈ 1 . 285153753372032 But how do we get results for the TSP? Let me explain by analogy to... Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  47. Physics of Algorithms, Santa Fe 2009 Refusal Chess 8 0a0Z0s0Z 7 o0Z0j0Z0 6 0ZRZ0Z0Z 5 Z0O0Z0Zq 4 0L0ZpZpO 3 ZPZ0O0Z0 2 PZ0Z0ZBZ 1 Z0Z0Z0J0 a b c d e f g h Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  48. Physics of Algorithms, Santa Fe 2009 In Refusal Chess, when a player makes a move, the opponent can either accept (and play on) or refuse, in case the move is taken back and the player has to choose another move. A player has the right to refuse once per move. For the chess players: A player is in check or checkmate if they would be in ordinary chess (so the rules are kind of illogical; for instance you cannot leave your king threatened just because you can refuse your opponent to capture it in the next move). If a player has only one legal move, the opponent cannot refuse it. Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  49. Physics of Algorithms, Santa Fe 2009 Refusal Chess 8 0Z0Z0s0Z 7 o0Z0j0Z0 6 0ZRZ0Z0Z 5 Z0O0Z0Zq 4 0L0ZpZpO 3 ZPZ0O0Z0 2 PZ0Z0ZBa 1 Z0Z0Z0J0 a b c d e f g h Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  50. Physics of Algorithms, Santa Fe 2009 Refusal Chess 8 0a0Z0s0Z 7 o0Z0j0Z0 6 0ZRZ0Z0Z 5 Z0O0Z0Zq 4 0L0ZpZpO 3 ZPZ0O0Z0 2 PZ0Z0ZBZ 1 Z0Z0Z0J0 a b c d e f g h Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  51. Physics of Algorithms, Santa Fe 2009 Refusal Chess 8 0a0Z0s0Z 7 o0Z0j0Z0 6 0ZRZ0Z0Z 5 Z0O0Z0Z0 4 0L0ZpZpl 3 ZPZ0O0Z0 2 PZ0Z0ZBZ 1 Z0Z0Z0J0 a b c d e f g h Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  52. Physics of Algorithms, Santa Fe 2009 Refusal Chess 8 0L0Z0s0Z 7 o0Z0j0Z0 6 0ZRZ0Z0Z 5 Z0O0Z0Z0 4 0Z0ZpZpl 3 ZPZ0O0Z0 2 PZ0Z0ZBZ 1 Z0Z0Z0J0 a b c d e f g h Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  53. Physics of Algorithms, Santa Fe 2009 This would be a bad move in ordinary chess since Black can simply take back with the rook. But in refusal chess it is not so clear... Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  54. Physics of Algorithms, Santa Fe 2009 Refusal Chess 8 0L0Z0s0Z 7 o0Z0j0Z0 6 0ZRZ0Z0Z 5 Z0O0Z0Z0 4 0Z0ZpZpZ 3 ZPZ0O0Z0 2 PZ0Z0ZBZ 1 Z0Z0l0J0 a b c d e f g h Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  55. Physics of Algorithms, Santa Fe 2009 Black accepts White’s move and plays on. Now White is in trouble, since refusing this move will allow Black to capture White’s queen. But let us not analyze this particular position further. The original position is taken from a spectacular finish by Jonathan Yedidia, one of the participants of the conference and former chess pro. He played 32 — Bh2+! and White resigned in view of 33. Kh1 Qxh4!, after which 34. Qb7+ (or any other move) is countered with a deadly discovered check. Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  56. Physics of Algorithms, Santa Fe 2009 My tour is better than yours! Alice and Bob play “My tour is better than yours!” Bob has this edge in his tour Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  57. Physics of Algorithms, Santa Fe 2009 My tour is better than yours! Alice and Bob play “My tour is better than yours!” Alice says: “Good for you, but I have this edge in my tour!” Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  58. Physics of Algorithms, Santa Fe 2009 My tour is better than yours! Alice and Bob play “My tour is better than yours!” Bob says: “Well, so do I”, effectively cancelling Alice’s move (this is the difference from Graph Exploration) Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  59. Physics of Algorithms, Santa Fe 2009 My tour is better than yours! Alice and Bob play “My tour is better than yours!” “Allright”, says Alice, “but I have this edge and you don’t!” Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  60. Physics of Algorithms, Santa Fe 2009 My tour is better than yours! Alice and Bob play “My tour is better than yours!” Bob has already admitted having two edges from that vertex, so he cannot cancel this move. Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  61. Physics of Algorithms, Santa Fe 2009 My tour is better than yours! Alice and Bob play “My tour is better than yours!” Similarly, Alice can refuse one of Bob’s moves by claiming that she also has this edge in her tour. Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  62. Physics of Algorithms, Santa Fe 2009 My tour is better than yours! Alice and Bob play “My tour is better than yours!” But if she does... Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  63. Physics of Algorithms, Santa Fe 2009 My tour is better than yours! Alice and Bob play “My tour is better than yours!” ...she will have to accept Bob’s second move, and so on... Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  64. Physics of Algorithms, Santa Fe 2009 Here I am sweeping a number of details under the rug, but the structure of the game is the same as in Refusal Chess (you always play your second best move; we can call the game “Refusal Exploration”). The results for this game and the conclusions for the TSP are analogous to the results for matching. Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  65. Physics of Algorithms, Santa Fe 2009 The end is near... Future work Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  66. Physics of Algorithms, Santa Fe 2009 The end is near... Future work 0 < d < 1 Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  67. Physics of Algorithms, Santa Fe 2009 The end is near... Future work 0 < d < 1 Games for other optimization problems (edge cover?) Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  68. Physics of Algorithms, Santa Fe 2009 The end is near... Future work 0 < d < 1 Games for other optimization problems (edge cover?) Efficiency of Belief Propagation? Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

  69. Physics of Algorithms, Santa Fe 2009 The end is near... Future work 0 < d < 1 Games for other optimization problems (edge cover?) Efficiency of Belief Propagation? Computer analysis of games Johan W¨ astlund Replica Symmetry and Combinatorial Optimization

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Simulated Annealing Key idea: Vary temperature parameter, i.e. , probability of accepting

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Simulated Annealing Key idea: Vary temperature parameter, i.e. , probability of accepting worsening moves, in Probabilistic Iterative Improvement according to annealing schedule (aka cooling schedule ). Inspired by physical annealing process: I

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