Dynamic programming computes optimal solutions to complex combinatorial problems through decomposition into overlapping subproblems with optimal substructure. In the Longest Common Subsequence (LCS) matrix, each table coordinate DP[i][j] caches the maximal length of common subsequences for prefixes of length i and j. Caching intermediate evaluations prevents exponential combinatorial recomputation, bounding overall complexity to polynomial time.