Ronald L. Rivest

Ronald L. Rivest is Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology.

  • Introduction to Algorithms, Third Edition

    Introduction to Algorithms, Third Edition

    Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein

    A new edition of the essential text and professional reference, with substantial new material on such topics as vEB trees, multithreaded algorithms, dynamic programming, and edge-based flow.

    Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. The book covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.

    The first edition became a widely used text in universities worldwide as well as the standard reference for professionals. The second edition featured new chapters on the role of algorithms, probabilistic analysis and randomized algorithms, and linear programming. The third edition has been revised and updated throughout. It includes two completely new chapters, on van Emde Boas trees and multithreaded algorithms, substantial additions to the chapter on recurrence (now called “Divide-and-Conquer”), and an appendix on matrices. It features improved treatment of dynamic programming and greedy algorithms and a new notion of edge-based flow in the material on flow networks. Many new exercises and problems have been added for this edition. The international paperback edition is no longer available; the hardcover is available worldwide.

    • Hardcover $99.00
    • Paperback $80.00
  • Introduction to Algorithms, Second Edition

    Introduction to Algorithms, Second Edition

    Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein

    There are books on algorithms that are rigorous but incomplete and others that cover masses of material but lack rigor. Introduction to Algorithms combines rigor and comprehensiveness. The book covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor. The first edition became the standard reference for professionals and a widely used text in universities worldwide. The second edition features new chapters on the role of algorithms, probabilistic analysis and randomized algorithms, and linear programming, as well as extensive revisions to virtually every section of the book. In a subtle but important change, loop invariants are introduced early and used throughout the text to prove algorithm correctness. Without changing the mathematical and analytic focus, the authors have moved much of the mathematical foundations material from Part I to an appendix and have included additional motivational material at the beginning.

    • Hardcover $85.00
    • Paperback $62.00
  • Computational Learning Theory and Natural Learning Systems, Volume 2

    Computational Learning Theory and Natural Learning Systems, Volume 2

    Intersections between Theory and Experiment

    Stephen José Hanson, Michael J. Kearns, Thomas Petsche, and Ronald L. Rivest

    As with Volume I, this second volume represents a synthesis of issues in three historically distinct areas of learning research: computational learning theory, neural network research, and symbolic machine learning. While the first volume provided a forum for building a science of computational learning across fields, this volume attempts to define plausible areas of joint research: the contributions are concerned with finding constraints for theory while at the same time interpreting theoretic results in the context of experiments with actual learning systems. Subsequent volumes will focus on areas identified as research opportunities.

    Computational learning theory, neural networks, and AI machine learning appear to be disparate fields; in fact they have the same goal: to build a machine or program that can learn from its environment. Accordingly, many of the papers in this volume deal with the problem of learning from examples. In particular, they are intended to encourage discussion between those trying to build learning algorithms (for instance, algorithms addressed by learning theoretic analyses are quite different from those used by neural network or machine-learning researchers) and those trying to analyze them.

    The first section provides theoretical explanations for the learning systems addressed, the second section focuses on issues in model selection and inductive bias, the third section presents new learning algorithms, the fourth section explores the dynamics of learning in feedforward neural networks, and the final section focuses on the application of learning algorithms.

    A Bradford Book

  • Computational Learning Theory and Natural Learning Systems, Volume 1

    Computational Learning Theory and Natural Learning Systems, Volume 1

    Constraints and Prospects

    George Drastal, Stephen José Hanson, and Ronald L. Rivest

    These original contributions converge on an exciting and fruitful intersection of three historically distinct areas of learning research: computational learning theory, neural networks, and symbolic machine learning. Bridging theory and practice, computer science and psychology, they consider general issues in learning systems that could provide constraints for theory and at the same time interpret theoretical results in the context of experiments with actual learning systems.In all, nineteen chapters address questions such as, What is a natural system? How should learning systems gain from prior knowledge? If prior knowledge is important, how can we quantify how important? What makes a learning problem hard? How are neural networks and symbolic machine learning approaches similar? Is there a fundamental difference in the kind of task a neural network can easily solve as opposed to those a symbolic algorithm can easily solve?

  • Research Directions in Computer Science, Second Edition

    An MIT Perspective

    John V. Guttag, Albert Meyer, Ronald L. Rivest, and Peter Szolovits

    Research Directions in Computer Science celebrates the twenty-fifth anniversary of the founding of MIT's Project MAC. It covers the full range of ongoing computer science research at the MIT Laboratory for Computer Science and the MIT Artificial Intelligence Laboratory, both of which grew out of the original Project MAC. Leading researchers from the faculties and staffs of the laboratories highlight current research and future activities in multiprocessors and parallel computer architectures, in languages and systems for distributed computing, in intelligent systems (AI) and robotics, in complexity and learning theory, in software methodology, in programming language theory, in software for engineering research and education, and in the relation between computers and economic productivity.

    Contributors Abelson, Arvind, Rodney Brooks, David Clark, Fernando Corbato, William Daily, Michael Dertouzos, John Guttag, Berthold K. P. Horn, Barbara Liskov, Albert Meyer, Nicholas Negroponte, Marc Raibert, Ronald Rivest, Michael Sipser, Gerald Sussman, Peter Szolovits, and John Updike