Dynamic Programming: Difference between revisions
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* [[Algorithms#Dynamic_Programming_Algorithms|Algorithms]] | |||
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Dynamic programming algorithms are optimized recursive algorithms, where we store the solution of smaller subproblems and use them in computing the solution for larger subproblems, avoiding duplicate work and yielding superior running times. The classic example where the difference between the straightforward recursive solution and the corresponding dynamic programming solution is obvious is computing [[Fibonacci_Numbers|Fibonacci numbers]]. | Dynamic programming algorithms are optimized recursive algorithms, where we store the solution of smaller subproblems and use them in computing the solution for larger subproblems, avoiding duplicate work and yielding superior running times. The classic example where the difference between the straightforward recursive solution and the corresponding dynamic programming solution is obvious is computing [[Fibonacci_Numbers|Fibonacci numbers]]. | ||
Revision as of 21:49, 11 November 2021
External
Internal
Overview
Dynamic programming algorithms are optimized recursive algorithms, where we store the solution of smaller subproblems and use them in computing the solution for larger subproblems, avoiding duplicate work and yielding superior running times. The classic example where the difference between the straightforward recursive solution and the corresponding dynamic programming solution is obvious is computing Fibonacci numbers.