Dynamic Programming — Rod Cutting, Matrix Chain Multiplication, LCS, and Optimal BSTs | Chapter 14 of Intro to Algorithms
Dynamic Programming — Rod Cutting, Matrix Chain Multiplication, LCS, and Optimal BSTs | Chapter 14 of Intro to Algorithms Dynamic programming (DP) is a powerful algorithmic technique that revolutionizes how we solve complex optimization problems by decomposing them into overlapping subproblems and storing solutions to avoid redundant computation. Chapter 14 of introduction to algorithms explores the essential principles of dynamic programming, provides classic problem examples such as rod cutting, matrix-chain multiplication, longest common subsequence (LCS), and optimal binary search trees, and explains why DP is a core concept for computer scientists and programmers. For a concise video walkthrough and visual examples, watch the chapter summary below. Don’t forget to subscribe to Last Minute Lecture for more algorithm deep-dives. What Is Dynamic Programming? Dynamic programming is used to efficiently solve problems that feature both overlapping subproblems and opti...