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  2. Dynamic programming - Wikipedia

    en.wikipedia.org/wiki/Dynamic_programming

    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.

  3. Dynamic programming language - Wikipedia

    en.wikipedia.org/wiki/Dynamic_programming_language

    A dynamic programming language is a type of programming language that allows various operations to be determined and executed at runtime. This is different from the compilation phase. This is different from the compilation phase.

  4. Bellman equation - Wikipedia

    en.wikipedia.org/wiki/Bellman_equation

    The dynamic programming approach describes the optimal plan by finding a rule that tells what the controls should be, given any possible value of the state. For example, if consumption ( c ) depends only on wealth ( W ), we would seek a rule c ( W ) {\displaystyle c(W)} that gives consumption as a function of wealth.

  5. Longest common subsequence - Wikipedia

    en.wikipedia.org/wiki/Longest_common_subsequence

    For an arbitrary number of input sequences, the dynamic programming approach gives a solution in O ( N ∏ i = 1 N n i ) . {\displaystyle O\left(N\prod _{i=1}^{N}n_{i}\right).} There exist methods with lower complexity, [ 3 ] which often depend on the length of the LCS, the size of the alphabet, or both.

  6. Optimal substructure - Wikipedia

    en.wikipedia.org/wiki/Optimal_substructure

    In the application of dynamic programming to mathematical optimization, Richard Bellman's Principle of Optimality is based on the idea that in order to solve a dynamic optimization problem from some starting period t to some ending period T, one implicitly has to solve subproblems starting from later dates s, where t<s<T. This is an example of ...

  7. Change-making problem - Wikipedia

    en.wikipedia.org/wiki/Change-making_problem

    The following is a dynamic programming implementation (with Python 3) which uses a matrix to keep track of the optimal solutions to sub-problems, and returns the minimum number of coins, or "Infinity" if there is no way to make change with the coins given. A second matrix may be used to obtain the set of coins for the optimal solution.

  8. Dynamic problem (algorithms) - Wikipedia

    en.wikipedia.org/wiki/Dynamic_problem_(algorithms)

    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 initially constructed in time O(N log N) and provides insertion, deletion and query times in O(log N).

  9. Category:Dynamic programming - Wikipedia

    en.wikipedia.org/wiki/Category:Dynamic_programming

    Pages in category "Dynamic programming" The following 48 pages are in this category, out of 48 total. This list may not reflect recent changes. ...