Introduction to Algorithms 4th Edition by Cormen, Leiserson, Rivest & Stein (2022)

Introduction to Algorithms, 4th Edition (2022) by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein — commonly known as CLRS — is the definitive textbook on algorithms, with over 1 million copies sold worldwide and an Amazon #1 Bestseller in Computer Algorithms. Published by The MIT Press, this landmark reference has been the standard algorithms text in universities worldwide and the go-to reference for software engineers and computer scientists for more than three decades.

Some books on algorithms are rigorous but incomplete; others cover enormous amounts of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. It covers a broad range of algorithms in depth, yet makes their design and analysis accessible to readers at every level, with self-contained chapters and algorithms presented in clear pseudocode.

New for the fourth edition: new chapters on matchings in bipartite graphs, online algorithms, and machine learning; new material on topics including solving recurrence equations, hash tables, potential functions, and suffix arrays; 140 new exercises and 22 new problems; and refreshed treatment of dynamic programming and graph algorithms.

Topics covered in depth: the role of algorithms in computing; analyzing algorithms and asymptotic notation; divide-and-conquer; probabilistic analysis and randomized algorithms; sorting and order statistics including heapsort, quicksort, and linear-time sorting; hash tables; binary search trees, red-black trees, augmented data structures; dynamic programming; greedy algorithms; amortized analysis; elementary graph algorithms; minimum spanning trees; single-source and all-pairs shortest paths; maximum flow; matchings in bipartite graphs; multithreaded algorithms; online algorithms; machine learning; matrix operations; linear programming; number-theoretic algorithms; string matching; computational geometry; NP-completeness and approximation algorithms.

Essential for: undergraduate and graduate computer science algorithms courses; competitive programming preparation (ACM-ICPC, Codeforces, LeetCode); software engineering interview preparation at top tech companies (Google, Meta, Amazon, Microsoft, Apple); theoretical computer science research; data structures and algorithms bootcamps; and practicing software engineers seeking authoritative coverage of classical and modern algorithms.