Introduction to Algorithms

What the book covers

The book runs to over 1,300 pages and moves from foundations to advanced topics:

• Foundations: asymptotic notation, recurrences, divide and conquer, and probabilistic analysis.
• Sorting and order statistics: heapsort, quicksort, linear time sorting and selection.
• Data structures: hash tables, binary search trees, red-black trees and augmenting data structures.
• Design techniques: dynamic programming, greedy algorithms and amortised analysis.
• Advanced data structures and graph algorithms: search, minimum spanning trees, shortest paths and maximum flow.
• Selected topics: parallel algorithms, string matching, computational geometry, number theory, NP-completeness and approximation algorithms.

The fourth edition, published in 2022, adds new chapters on matchings in bipartite graphs, online algorithms and machine learning, new material on topics such as recurrences, hash tables and suffix arrays, and 140 new exercises and 22 new problems, according to the publisher. It also uses colour figures.

Is it the right book for you?

It is excellent when you need rigour: exact pseudocode, correctness proofs and precise running time analysis. It is the wrong choice if you are learning algorithms for the first time on your own, or preparing for coding problems under time pressure. The writing is precise but dense, and the maths assumes comfort with proofs, summations and basic probability.

Goodreads shows an average of 4.35 out of 5 from 9,398 ratings as of September 2026. That is a strong score for a dense, mathematical textbook, and it reflects how widely it is used as the course text in university algorithms classes.

How to use it well

• Take it with a course. MIT OpenCourseWare publishes lectures for its algorithms courses that follow this book's style; university courses often assign chapters from it.
• Read selectively. Look up the chapter for the algorithm you are studying, work through the pseudocode by hand, then implement it.
• Do some exercises. The exercises at the end of sections are where understanding forms. Pick a few per section rather than all of them.
• Keep it as a reference. When a friendlier source leaves you unsure about a detail, this is where you check.

Friendlier alternatives

For self study, books with more intuition and less formality, such as Skiena's Algorithm Design Manual or Sedgewick and Wayne's Algorithms, are easier going. Visual and course based introductions are better for a first pass. Many people learn from those and turn to this book for precision.

Common questions

Should I read it cover to cover? Almost nobody should. Read the chapters your course or work needs.

Is the third edition fine? For most topics, yes; the core material is the same. The fourth adds new chapters and exercises and fixes errors.

Do I need it for coding interviews? No. It goes much deeper than interviews require, although the basics chapters help.

Best for: CS students in an algorithms course and engineers who want the rigorous reference on their shelf.