Last Minute Lecture

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...

Red-Black Trees — Properties, Rotations, Insertion, and Deletion Explained | Chapter 13 of Intro to Algorithms Red-black trees are a class of self-balancing binary search trees that ensure fast operations for dynamic sets and dictionaries. By enforcing a series of color-based structural properties and local tree transformations, red-black trees guarantee O(log n) running time for SEARCH, INSERT, DELETE, and related queries. Chapter 13 of introduction to algorithms provides a comprehensive look at the mechanics, cases, and practical applications of red-black trees—an essential data structure in modern programming. For a concise video summary and step-by-step walkthrough of red-black trees, watch below—and subscribe to Last Minute Lecture for more clear algorithm guides. What Is a Red-Black Tree? A red-black tree is a binary search tree (BST) that maintains strict balancing through five color-based properties: Each node is either red or black . The root is alway...