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Tournament Trees

Tournament Trees. Winner trees. Loser Trees. Winner Tree – Definition. Complete binary tree with n-1 internal nodes and n external nodes. External nodes represent tournament players.

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Tournament Trees

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  1. Tournament Trees Winner trees. Loser Trees.

  2. Winner Tree – Definition Complete binary tree with n-1 internal nodes and n external nodes. External nodes represent tournament players. Each internal node represents a match played between its two children; the winner of the match is stored at the internal node. Root has overall winner.

  3. player match node Winner Tree For 16 Players

  4. 1 Winner Tree For 16 Players 1 2 3 1 2 2 3 6 1 3 2 4 2 5 4 3 6 8 1 5 7 3 2 6 9 4 5 2 5 8 Smaller element wins =>min winner tree.

  5. 1 Winner Tree For 16 Players 1 2 3 1 2 2 3 6 1 3 2 4 2 5 4 3 6 8 1 5 7 3 2 6 9 4 5 2 5 8 height is log2 n (excludes player level)

  6. Complexity Of Initialize • O(1)time to play match at each match node. • n – 1 match nodes. • O(n) time to initialize n-player winner tree.

  7. Winner Tree Operations • Initialize • O(n) time • Get winner • O(1) time • Replace winner and replay • O(log n)time • More preciselyTheta(log n) • Tie breaker (player on left wins in case of a tie).

  8. 1 1 2 3 1 2 2 3 6 1 3 2 4 2 5 4 3 6 8 1 5 7 3 2 6 9 4 5 2 5 8 Replace Winner And Replay Replace winner with 6.

  9. 1 Replace Winner And Replay 1 2 3 1 2 2 3 6 1 3 2 4 2 5 4 3 6 8 6 5 7 3 2 6 9 4 5 2 5 8 Replay matches on path to root.

  10. 1 Replace Winner And Replay 1 2 3 1 2 2 3 6 1 3 2 4 2 5 4 3 6 8 6 5 7 3 2 6 9 4 5 2 5 8 Replay matches on path to root.

  11. 1 Replace Winner And Replay 1 2 3 1 2 2 3 6 1 3 2 4 2 5 4 3 6 8 6 5 7 3 2 6 9 4 5 2 5 8 Opponent is player who lost last match played at this node.

  12. Loser Tree Each match node stores the match loser rather than the match winner.

  13. Min Loser Tree For 16 Players 3 4 8 4 3 6 8 1 5 7 3 2 6 9 4 5 2 5 8

  14. Min Loser Tree For 16 Players 3 6 1 4 8 5 7 4 3 6 8 1 5 7 3 2 6 9 4 5 2 5 8

  15. 1 Min Loser Tree For 16 Players 3 6 3 2 4 8 5 7 6 9 4 3 6 8 1 5 7 3 2 6 9 4 5 2 5 8

  16. 1 Min Loser Tree For 16 Players 3 2 6 3 4 2 4 8 5 7 6 9 5 8 4 3 6 8 1 5 7 3 2 6 9 4 5 2 5 8

  17. 1 Min Loser Tree For 16 Players 3 2 6 3 4 5 4 8 5 7 6 9 5 8 4 3 6 8 1 5 7 3 2 6 9 4 5 2 5 8

  18. 1 Min Loser Tree For 16 Players 3 2 6 3 4 5 4 8 5 7 6 9 5 8 4 3 6 8 1 5 7 3 2 6 9 4 5 2 5 8

  19. 2 Min Loser Tree For 16 Players 3 2 6 3 4 5 4 8 5 7 6 9 5 8 4 3 6 8 1 5 7 3 2 6 9 4 5 2 5 8

  20. Winner 1 2 3 2 6 3 4 5 4 8 5 7 6 9 5 8 4 3 6 8 1 5 7 3 2 6 9 4 5 2 5 8

  21. Complexity Of Loser Tree Initialize • Start with 2 credits at each match node. • Use one to pay for the match played at that node. • Use the other to pay for the store of a left child winner. • Total time is O(n). • More preciselyTheta(n).

  22. 2 3 5 9 3 1 2 3 2 6 3 4 5 4 8 5 7 6 9 5 8 4 3 6 8 1 5 7 3 2 6 9 4 5 2 5 8 9 Winner Replace winner with 9 and replay matches.

  23. Complexity Of Replay • One match at each level that has a match node. • O(log n) • More preciselyTheta(log n).

  24. Tournament Tree Applications • Run generation. • k-way merging of runs during an external merge sort. • Truck loading.

  25. Truck Loading • n packages to be loaded into trucks • each package has a weight • each truck has a capacity of c tons • minimize number of trucks

  26. Bin Packing • n items to be packed into bins • each item has a size • each bin has a capacity of c • minimize number of bins

  27. Bin Packing Truck loading is same as bin packing. Truck is a bin that is to be packed (loaded). Package is an item/element. Bin packing to minimize number of bins is NP-hard. Several fast heuristics have been proposed.

  28. Bin Packing Heuristics • First Fit. • Bins are arranged in left to right order. • Items are packed one at a time in given order. • Current item is packed into leftmost bin into which it fits. • If there is no bin into which current item fits, start a new bin.

  29. Bin Packing Heuristics • First Fit Decreasing. • Items are sorted into decreasing order. • Then first fit is applied.

  30. Bin Packing Heuristics • Best Fit. • Items are packed one at a time in given order. • To determine the bin for an item, first determine setSofbins into which the item fits. • If S is empty, then start a new bin and put item into this new bin. • Otherwise, pack into bin ofSthat has least available capacity.

  31. Bin Packing Heuristics • Best Fit Decreasing. • Items are sorted into decreasing order. • Then best fit is applied.

  32. Performance • For first fit and best fit: Heuristic Bins <= (17/10)(Minimum Bins) + 2 • For first fit decreasing and best fit decreasing: Heuristic Bins <= (11/9)(Minimum Bins) + 4

  33. 9 8 9 8 7 9 8 4 8 5 7 6 9 5 8 4 3 6 8 1 5 7 3 2 6 9 4 5 2 5 8 Max Winner-Tree For 16 Bins Item size = 7

  34. 9 Max Winner-Tree For 16 Bins 7 9 6 7 9 8 4 6 5 7 6 9 5 8 4 3 6 1 1 5 7 3 2 6 9 4 5 2 5 8

  35. Complexity Of First Fit O(n log n), where n is the number of items.

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