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From a dynamic programming point of view, Dijkstra's algorithm for the shortest path problem is a successive approximation scheme that solves the dynamic programming functional equation for the shortest path problem by the Reaching method. [8] [9] [10] In fact, Dijkstra's explanation of the logic behind the algorithm, [11] namely Problem 2.
Static problem For a set of N numbers find the maximal one. The problem may be solved in O(N) time. Dynamic problem For an initial set of N numbers, dynamically maintain the maximal one when insertion and deletions are allowed. A well-known solution for this problem is using a self-balancing binary search tree. It takes space O(N), may be ...
Dynamic Programming: problems exhibiting the properties of overlapping subproblems and optimal substructure; Ellipsoid method: is an algorithm for solving convex optimization problems; Evolutionary computation: optimization inspired by biological mechanisms of evolution Evolution strategy; Gene expression programming; Genetic algorithms
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Generalized assignment problem; Integer programming. The variant where variables are required to be 0 or 1, called zero-one linear programming, and several other variants are also NP-complete [2] [3]: MP1 Some problems related to Job-shop scheduling; Knapsack problem, quadratic knapsack problem, and several variants [2] [3]: MP9
One of the earliest applications of dynamic programming is the Held–Karp algorithm, which solves the problem in time (). [24] This bound has also been reached by Exclusion-Inclusion in an attempt preceding the dynamic programming approach. Solution to a symmetric TSP with 7 cities using brute force search.
Dynamic programming. Bellman equation; Hamilton–Jacobi–Bellman equation — continuous-time analogue of Bellman equation; Backward induction — solving dynamic programming problems by reasoning backwards in time; Optimal stopping — choosing the optimal time to take a particular action Odds algorithm; Robbins' problem; Global optimization ...
The dynamic programming method breaks this decision problem into smaller subproblems. Bellman's principle of optimality describes how to do this: Principle of Optimality: An optimal policy has the property that whatever the initial state and initial decision are, the remaining decisions must constitute an optimal policy with regard to the state ...