class_3_dynamic_programming(5).pdf - Dynamic programming...

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Dynamic programming Dynamic programming A few words about Dynamic programming Longest common subsequence The 0-1 Knapsack problem Dijkstra’s algorithm Dynamic programming August 16, 2017
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Dynamic programming Dynamic programming A few words about Dynamic programming Longest common subsequence The 0-1 Knapsack problem Dijkstra’s algorithm Overview 1 Dynamic programming A few words about Dynamic programming Longest common subsequence The 0-1 Knapsack problem Dijkstra’s algorithm
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Dynamic programming Dynamic programming A few words about Dynamic programming Longest common subsequence The 0-1 Knapsack problem Dijkstra’s algorithm Dynamic programming Like the divide and conquer approach, dynamic programming also breaks a problem into subproblems, solves the subproblems, and than combine the solutions. Unlike divide and conquer, the subproblems in dynamic programming are not disjoint - they overlap. A dynamic programming algorithm, solves each subproblem and saves the solution in a table. This makes it possible to avoid recalculating the common parts over and over again. Dynamic programming approach is often suitable for optimization problems. These are problems where we look for an optimal (minimal or maximal) solution among many possible solutions.
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Dynamic programming Dynamic programming A few words about Dynamic programming Longest common subsequence The 0-1 Knapsack problem Dijkstra’s algorithm Dynamic programming Like the divide and conquer approach, dynamic programming also breaks a problem into subproblems, solves the subproblems, and than combine the solutions. Unlike divide and conquer, the subproblems in dynamic programming are not disjoint - they overlap. A dynamic programming algorithm, solves each subproblem and saves the solution in a table. This makes it possible to avoid recalculating the common parts over and over again. Dynamic programming approach is often suitable for optimization problems. These are problems where we look for an optimal (minimal or maximal) solution among many possible solutions.
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Dynamic programming Dynamic programming A few words about Dynamic programming Longest common subsequence The 0-1 Knapsack problem Dijkstra’s algorithm Dynamic programming Like the divide and conquer approach, dynamic programming also breaks a problem into subproblems, solves the subproblems, and than combine the solutions. Unlike divide and conquer, the subproblems in dynamic programming are not disjoint - they overlap. A dynamic programming algorithm, solves each subproblem and saves the solution in a table. This makes it possible to avoid recalculating the common parts over and over again. Dynamic programming approach is often suitable for optimization problems. These are problems where we look for an optimal (minimal or maximal) solution among many possible solutions.
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Dynamic programming Dynamic programming A few words about Dynamic programming Longest common subsequence The 0-1 Knapsack problem Dijkstra’s algorithm Dynamic programming
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