slides

More documents

Recommendations

Info

Dijkstra Algorithm • keep a cut (S : V - S) where S vertices are fixed • maintain a priority queue Q of V - S vertices • each iteration choose the best vertex v from Q • move v to S, and use d(v) to forward-update others ... d(u) ⊕ = d(v) ⊗ w(v, u) S V - S time complexity: O((V+E) lgV) (binary heap) O(V lgV + E) (fib. heap) Liang Huang 19 Dynamic Programming
Dijkstra Algorithm • keep a cut (S : V - S) where S vertices are fixed • maintain a priority queue Q of V - S vertices • each iteration choose the best vertex v from Q • move v to S, and use d(v) to forward-update others ... d(u) ⊕ = d(v) ⊗ w(v, u) S V - S time complexity: O((V+E) lgV) (binary heap) O(V lgV + E) (fib. heap) Liang Huang 19 Dynamic Programming
Page 1 and 2: Dynamic Programming Algorithms in S
Page 3 and 4: Dynamic Programming Liang Huang 3 D
Page 5 and 6: Dynamic Programming • Dynamic Pro
Page 7 and 8: Two Dimensional Survey traversing o
Page 9 and 10: Motivations for Semirings • in a
Page 11 and 12: Definitions A monoid is a triple (A
Page 13 and 14: Definitions A monoid is a triple (A
Page 15 and 16: Examples Semiring Set ⊕ ⊗ 0 1 i
Page 17 and 18: Ordering • idempotent 9 A semirin
Page 19 and 20: Ordering • idempotent A semiring
Page 21 and 22: Monotonicity • monotonicity 10 Li
Page 23 and 24: • monotonicity Monotonicity Let K
Page 31 and 32: Viterbi Algorithm for DAGs 1. topol
Page 33 and 34: Examples Liang Huang 15 Dynamic Pro
Page 35 and 36: Examples • [Number of Paths in a
Page 41 and 42: Example: Word Alignment Liang Huang
Page 43 and 44: Dijkstra Algorithm • Dijkstra doe
Page 49: Dijkstra Algorithm • Dijkstra doe
Page 53 and 54: Viterbi vs. Dijkstra • structural
Page 61 and 62: What if both work? monotonic optimi
Page 65 and 66: Background: CFG and Parsing Liang H
Page 67 and 68: Hypergraphs and Deduction tails : a
Page 69 and 70: Hypergraphs and Deduction tails : a
Page 71 and 72: Weight Functions and Semirings d(u)
Page 81 and 82: Viterbi Algorithm for DAGs 1. topol
Page 83 and 84: Example: CKY Parsing • parsing wi
Page 91 and 92: Forward Variant for DAHs 1. topolog
Page 93 and 94: Example: Treebank Parsers • State
Page 95 and 96: Dijkstra Algorithm • keep a cut (
Page 97 and 98: Knuth (1977) Algorithm • keep a c
Page 99 and 100: Knuth (1977) Algorithm • keep a c
Page 101 and 102:
Example: A* Parsing • Use A* sear
Page 103 and 104:
Same Picture Again monotonic optimi
Page 105 and 106:
Page 107 and 108:
Page 109:
Conclusion • surveyed two general
show all

slides

Create successful ePaper yourself

Delete template?

Save as template?