divideandconquer

Divide and conquer is a general algorithm design paradigm that solves a problem by dividing it into smaller subproblems of the same type, solving those subproblems independently (often recursively), and then combining their solutions to form an answer for the original problem. This approach is effective when subproblems are smaller instances of the same problem and when a straightforward merging step can assemble a correct solution from subresults.

The typical process involves three steps: divide the input into a number of smaller subproblems, solve each subproblem recursively, and then combine the subproblem solutions to form the final result. The recursion continues until reaching base cases that are trivial to solve. This structure often leads to elegant and modular implementations, and the subproblems can frequently be solved in parallel.

Complexity is commonly analyzed with recurrences of the form T(n) = a T(n/b) + f(n), where a is the number of subproblems, each of size n/b, and f(n) accounts for dividing the problem and merging results. The Master Theorem provides a compact way to bound T(n) for many standard instances.

Classic examples include binary search (split the input in half), merge sort (divide, sort, and merge), and quicksort (partition around a pivot and recursively sort partitions). Other notable divide-and-conquer algorithms include Karatsuba multiplication, Strassen matrix multiplication, and the fast Fourier transform.

Advantages of this paradigm include simplicity, modularity, and easy parallelization. Limitations arise when subproblems are not independent or when the merging step is inefficient; in such cases dynamic programming, memoization, or alternative strategies may be more appropriate.