Dynamic Programming
Understand how dynamic programming optimizes recursive solutions by storing subproblem results, reducing redundant calculations, and improving efficiency. Explore key concepts like optimal substructure and overlapping subproblems through examples such as Fibonacci numbers and longest common subsequence, and learn approaches like tabulation and memoization to solve algorithmic challenges.
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Dynamic programming is mainly used to provide optimization for recursion. In the recursive solution, if we are using the function with the same parameters, we can avoid re-computation with dynamic programming. The concept in dynamic programming is to store the subproblem solution and use it in a later part of processing.
We can understand dynamic programming by getting the ith Fibonacci number.
The Fibonacci series contains numbers that have the following relation:
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