Algorithms from THE BOOK
Algorithms from THE BOOK

Author: Kenneth Lange

Publisher: SIAM

Published: 2020-05-04

Total Pages: 227

ISBN-13: 1611976170

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Algorithms are a dominant force in modern culture, and every indication is that they will become more pervasive, not less. The best algorithms are undergirded by beautiful mathematics. This text cuts across discipline boundaries to highlight some of the most famous and successful algorithms. Readers are exposed to the principles behind these examples and guided in assembling complex algorithms from simpler building blocks. Written in clear, instructive language within the constraints of mathematical rigor, Algorithms from THE BOOK includes a large number of classroom-tested exercises at the end of each chapter. The appendices cover background material often omitted from undergraduate courses. Most of the algorithm descriptions are accompanied by Julia code, an ideal language for scientific computing. This code is immediately available for experimentation. Algorithms from THE BOOK is aimed at first-year graduate and advanced undergraduate students. It will also serve as a convenient reference for professionals throughout the mathematical sciences, physical sciences, engineering, and the quantitative sectors of the biological and social sciences.

101 Algorithms Questions You Must Know
101 Algorithms Questions You Must Know

Author: Amrinder Arora

Publisher:

Published: 2018-12-29

Total Pages: 159

ISBN-13: 9781718157422

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"101 Algorithms Questions You Must Know" presents 101 asymptotic complexity Questions and Answers, organized by Algorithm Design Techniques. Serving as a useful accompaniment to "Analysis and Design of Algorithms" (ISBN 978-1516513086), the questions are distributed as follows: 9 Warm up Questions on Math Basics, 19 Questions on Asymptotic Analysis and Asymptotic Notation, 3 Questions on Data Structures, 17 Questions on Divide and Conquer, 8 Questions on Greedy Algorithms, 18 Questions on Dynamic Programming, 5 Questions on Graph Traversal (BFS/DFS), 4 Questions on Branch and Bound, 9 Questions on NP-Completeness 3 Questions on Lower Bounds, and 6 Questions on Graph Theory.Covering many questions used by major technology companies as their interview questions, this book serves both software professionals as well as graduate students in the field.

Algorithms Quiz Book
Algorithms Quiz Book

Author: S.R. Subramanya

Publisher: Wemocon

Published: 2021-11-12

Total Pages: 223

ISBN-13:

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This is a quick assessment book / quiz book. It has a vast collection of over 1,000 questions, with answers on Algorithms. The book covers questions on standard (classical) algorithm design techniques; sorting and searching; graph traversals; minimum spanning trees; shortest path problems; maximum flow problems; elementary concepts in P and NP Classes. It also covers a few specialized areas – string processing; polynomial operations; numerical & matrix computations; computational geometry & computer graphics.

Introduction to Algorithms, third edition
Introduction to Algorithms, third edition

Author: Thomas H. Cormen

Publisher: MIT Press

Published: 2009-07-31

Total Pages: 1313

ISBN-13: 0262258102

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The latest 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 exercises and problems have been added for this edition. The international paperback edition is no longer available; the hardcover is available worldwide.

Algorithms, Part II
Algorithms, Part II

Author: Robert Sedgewick

Publisher: Addison-Wesley Professional

Published: 2014-02-01

Total Pages: 973

ISBN-13: 0133847268

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This book is Part II of the fourth edition of Robert Sedgewick and Kevin Wayne’s Algorithms, the leading textbook on algorithms today, widely used in colleges and universities worldwide. Part II contains Chapters 4 through 6 of the book. The fourth edition of Algorithms surveys the most important computer algorithms currently in use and provides a full treatment of data structures and algorithms for sorting, searching, graph processing, and string processing -- including fifty algorithms every programmer should know. In this edition, new Java implementations are written in an accessible modular programming style, where all of the code is exposed to the reader and ready to use. The algorithms in this book represent a body of knowledge developed over the last 50 years that has become indispensable, not just for professional programmers and computer science students but for any student with interests in science, mathematics, and engineering, not to mention students who use computation in the liberal arts. The companion web site, algs4.cs.princeton.edu contains An online synopsis Full Java implementations Test data Exercises and answers Dynamic visualizations Lecture slides Programming assignments with checklists Links to related material The MOOC related to this book is accessible via the "Online Course" link at algs4.cs.princeton.edu. The course offers more than 100 video lecture segments that are integrated with the text, extensive online assessments, and the large-scale discussion forums that have proven so valuable. Offered each fall and spring, this course regularly attracts tens of thousands of registrants. Robert Sedgewick and Kevin Wayne are developing a modern approach to disseminating knowledge that fully embraces technology, enabling people all around the world to discover new ways of learning and teaching. By integrating their textbook, online content, and MOOC, all at the state of the art, they have built a unique resource that greatly expands the breadth and depth of the educational experience.

Algorithms to Live By
Algorithms to Live By

Author: Brian Christian

Publisher: Macmillan

Published: 2016-04-19

Total Pages: 366

ISBN-13: 1627790365

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'Algorithms to Live By' looks at the simple, precise algorithms that computers use to solve the complex 'human' problems that we face, and discovers what they can tell us about the nature and origin of the mind.

Python Algorithms
Python Algorithms

Author: Magnus Lie Hetland

Publisher: Apress

Published: 2014-09-17

Total Pages: 303

ISBN-13: 1484200551

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Python Algorithms, Second Edition explains the Python approach to algorithm analysis and design. Written by Magnus Lie Hetland, author of Beginning Python, this book is sharply focused on classical algorithms, but it also gives a solid understanding of fundamental algorithmic problem-solving techniques. The book deals with some of the most important and challenging areas of programming and computer science in a highly readable manner. It covers both algorithmic theory and programming practice, demonstrating how theory is reflected in real Python programs. Well-known algorithms and data structures that are built into the Python language are explained, and the user is shown how to implement and evaluate others.

Algorithms
Algorithms

Author: Sanjoy Dasgupta

Publisher: McGraw-Hill Higher Education

Published: 2006

Total Pages: 338

ISBN-13: 0077388496

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This text, extensively class-tested over a decade at UC Berkeley and UC San Diego, explains the fundamentals of algorithms in a story line that makes the material enjoyable and easy to digest. Emphasis is placed on understanding the crisp mathematical idea behind each algorithm, in a manner that is intuitive and rigorous without being unduly formal. Features include:The use of boxes to strengthen the narrative: pieces that provide historical context, descriptions of how the algorithms are used in practice, and excursions for the mathematically sophisticated. Carefully chosen advanced topics that can be skipped in a standard one-semester course but can be covered in an advanced algorithms course or in a more leisurely two-semester sequence.An accessible treatment of linear programming introduces students to one of the greatest achievements in algorithms. An optional chapter on the quantum algorithm for factoring provides a unique peephole into this exciting topic. In addition to the text DasGupta also offers a Solutions Manual which is available on the Online Learning Center."Algorithms is an outstanding undergraduate text equally informed by the historical roots and contemporary applications of its subject. Like a captivating novel it is a joy to read." Tim Roughgarden Stanford University

Deterministic Global Optimization
Deterministic Global Optimization

Author: Christodoulos A. Floudas

Publisher: Springer Science & Business Media

Published: 2000

Total Pages: 774

ISBN-13: 9780792360148

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This book provides a unified and insightful treatment of deterministic global optimization. It introduces theoretical and algorithmic advances that address the computation and characterization of global optima, determine valid lower and upper bounds on the global minima and maxima, and enclose all solutions of nonlinear constrained systems of equations. Among its special features, the book: Introduces the fundamentals of deterministic global optimization; Provides a thorough treatment of decomposition-based global optimization approaches for biconvex and bilinear problems; Covers global optimization methods for generalized geometric programming problems Presents in-depth global optimization algorithms for general twice continuously differentiable nonlinear problems; Provides a detailed treatment of global optimization methods for mixed-integer nonlinear problems; Develops global optimization approaches for the enclosure of all solutions of nonlinear constrained systems of equations; Includes many important applications from process design, synthesis, control, and operations, phase equilibrium, design under uncertainty, parameter estimation, azeotrope prediction, structure prediction in clusters and molecules, protein folding, and peptide docking. Audience: This book can be used as a textbook in graduate-level courses and as a desk reference for researchers in all branches of engineering and applied science, applied mathematics, industrial engineering, operations research, computer science, economics, computational chemistry and molecular biology.

Algorithms for Decision Making
Algorithms for Decision Making

Author: Mykel J. Kochenderfer

Publisher: MIT Press

Published: 2022-08-16

Total Pages: 701

ISBN-13: 0262047012

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A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them. Automated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them. The book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through interaction with the environment; state uncertainty, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple agents. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised learning and optimization. Algorithms are implemented in the Julia programming language. Figures, examples, and exercises convey the intuition behind the various approaches presented.