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To solve a problem using divide and conquer with recursion, you break the problem into smaller subproblems, solve them recursively, and combine their solutions.

Divide and conquer is a powerful algorithmic paradigm that solves a problem by breaking it into smaller subproblems, solving each subproblem independently, and combining their solutions to solve the original problem. This approach is often implemented with recursion, a programming technique where a function calls itself to solve a smaller version of the same problem.

The first step in a divide and conquer approach is to divide the problem into smaller subproblems. This is typically done by finding a way to break the problem down into two or more smaller problems. For example, if you're sorting a list of numbers, you might divide the list in half and sort each half independently.

Next, you solve each subproblem recursively. This means that you use the same divide and conquer approach to solve each subproblem. You keep breaking each subproblem down into smaller and smaller pieces until you reach a base case - a subproblem that is so small it can be solved directly without further division. In the sorting example, the base case might be a list with only one number, which is already sorted.

Finally, you combine the solutions to the subproblems to solve the original problem. This might involve merging sorted lists, adding up numbers, or some other operation that depends on the specific problem you're solving.

The key to successfully using divide and conquer with recursion is to ensure that each subproblem is a smaller instance of the same problem, and that the base case is chosen so that every possible chain of recursive calls will eventually reach it. This requires careful design and analysis of the algorithm, but when done correctly, it can lead to highly efficient and elegant solutions to complex problems.

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