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The concept of dynamic programming involves optimizing over plain recursion. Meaning, we can use it to optimize a recursive solution with a repeated call for the same output. In dynamic programming, we store the solutions of sub-problems to save us the stress of re-computing them again later when they are needed. Doing this reduces to a great extent the complexities of time from exponential to polynomial. For example, suppose we have written a recursive solution for Fibonacci numbers, we are bound to face a time complexity. However, we can reduce this time complexity to linear if we optimize the solution by storing the results. In simple terms, we can define dynamic programming as an algorithmic technique that solves a problem by breaking it into smaller sub-problems. The solutions to these sub-problems will determine the optimal solution to the main problem.
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