Dynamic Programming serves as a technique employed in the realms of mathematics and computer science for tackling intricate problems through their decomposition into more manageable subproblems. The methodology involves addressing each subproblem just once and retaining the outcomes, thus steering clear of repetitive computations. This approach results in enhanced efficiency when resolving an array of issues. To illustrate, consider the Fibonacci Numbers; employing a basic recursive solution leads to an exponential time complexity, but optimization through the storage of subproblem solutions reduces the time complexity to linear.
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