Dynamic Programing
Dynamic Programing Dynamic Programing
Dynamic Programming (DP) A method for efficiently solving optimization problems which have overlapping subproblems
Property of DP problems • Have overlapping subproblems • Have optimal solutions to subproblems • Can be represented in recurrence relation • Are context-independent e.g. In sequence alignment, quantifying similarity is only based on pairs of residues. Similarity is independent of environment of residues we align.
- Page 1 and 2: Dynamic Programming and Pairwise Se
- Page 3 and 4: Importance of Sequence Alignment
- Page 5 and 6: Alignment Operation Transforming on
- Page 7 and 8: Difficulties in measuring sequence
- Page 9 and 10: Efficient way to find a best alignm
- Page 11: Problems Solvable by Dynamic Progra
- Page 16 and 17: I. Global Alignment Classes of Pair
- Page 18 and 19: Classes of Pairwise Alignment: I. G
- Page 20 and 21: Classes of Pairwise Alignment: I. G
- Page 22 and 23: Global Alignment spans all the resi
- Page 24 and 25: Scoring matrix represents a specifi
- Page 26 and 27: Needleman-Wunsch Algorithm (Cont.)
- Page 28 and 29: Needleman-Wunsch Algorithm (Cont.)
- Page 30 and 31: Needleman-Wunsch Algorithm (Cont.)
- Page 32 and 33: Needleman-Wunsch Algorithm Efficien
- Page 34 and 35: Needleman-Wunsch Algorithm for any
- Page 36 and 37: Needleman-Wunsch Algorithm for any
- Page 38 and 39: Local Alignment finds the most simi
- Page 40 and 41: Smith-Waterman Algorithm (Cont.)
- Page 42 and 43: Smith-Waterman Algorithm (Cont.) v1
- Page 44 and 45: Smith-Waterman Algorithm (Cont.) Ex
- Page 46 and 47: Which alignment to use Example 1. O
- Page 48 and 49: Which alignment to use Example 1. O
- Page 50 and 51: Which alignment to use Example 1. O
- Page 52 and 53: Which alignment to use example 1 co
- Page 54 and 55: Which alignment to use (Cont.) Exam
- Page 56 and 57: Versatility of DP Algorithm • Mem
- Page 58 and 59: References - Gusfield D. Algorithms
<strong>Dynamic</strong> Programming (DP)<br />
A method for efficiently solving optimization<br />
problems which have overlapping subproblems
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