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Algorithms and Data Structures
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  • The Everyday Life of an Algorithm (Daniel Neyland)

    Through investigating the everyday life of the algorithm, the book opens a conversation with existing social science research that tends to focus on the power and opacity of algorithms, via unique access to the algorithm's design, development, testing, etc.

  • Engines of Order: A Mechanology of Algorithmic Techniques

    Ihis book examines the constructive and cumulative character of software and retraces the historical trajectories of a series of algorithmic techniques that have become the building blocks for contemporary practices of ordering.

  • The Constitution of Algorithms (Florian Jaton)

    A provocative and skillful study of how algorithms come into the world - and inevitably shape it. Jaton performs the daring feat of offering an empirically rich analysis of algorithms without taking them for granted.

  • Think Data Structures: Algorithms and Information Retrieval

    This practical book will help you learn and review some of the most important ideas in software engineering - data structures and algorithms - in a way that's clearer, more concise, and more engaging than other materials. Useful in technical interviews too.

  • Elementary Algorithms (Xinyu Liu)

    This book doesn't only focus on an imperative (or procedural) approach, but also includes purely functional algorithms and data structures. The examples are in multiple programming languages: Haskell, C, Python, C++, Scheme/Lisp.

  • Lecture Notes for the Algorithms (Jeff Erickson)

    This lecture notes uniquely combines rigor and comprehensiveness. It 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.

  • Algorithm Design (Jon Kleinberg, et al)

    This book introduces algorithms by looking at the real-world problems that motivate them. The book teaches a range of design and analysis techniques for problems that arise in computing applications.

  • Notes on Randomized Algorithms (James Aspnes)

    This book introduces the basic concepts in the design and analysis of randomized algorithms. Discusses tools from probability theory, including random variables and expectations, union bound arguments, concentration bounds, martingales, etc.

  • Algorithms for Reinforcement Learning (Csaba Szepesvari)

    This book focuses on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. It gives a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms.

  • Algorithmic Aspects of Machine Learning (Ankur Moitra)

    This book bridges theoretical computer science and machine learning by exploring what the two sides can teach each other. It emphasizes the need for flexible, tractable models that better capture not what makes machine learning hard, but what makes it easy.

  • Algorithmic Composition: Introduction to Music Composition

    This book provides an overview of procedural approaches to music generation. It introduces programming concepts through many examples written using the Common LISP and Common Music for music composition and sound synthesis.

  • Open Data Structures: An Introduction (Pat Morin)

    This book is an introduction to the field of data structures and algorithms, it covers the implementation and analysis of data structures for sequences (lists), queues, priority queues, unordered dictionaries, ordered dictionaries, and graphs.

  • Algorithms: Fundamental Techniques (Macneil Shonle, et al)

    The goal of the book is to show you how you can methodically apply different techniques to your own algorithms to make them more efficient. While this book mostly highlights general techniques, some well-known algorithms are also looked at in depth.

  • Algorithms and Data Structures: The Basic Toolbox (Kurt Mehlhorn)

    This book is a concise introduction addressed to students and professionals familiar with programming and basic mathematical language. The algorithms are presented in a modern way, with explicitly formulated invariants, and comment on recent trends.

  • Purely Functional Data Structures (Chris Okasaki)

    This book describes data structures from the point of view of functional languages, with examples, and presents design techniques that allow programmers to develop their own functional data structures. All source code is given in Standard ML and Haskell.

  • Algorithms and Data Structures (Niklaus Wirth)

    From the inventor of Pascal and Modula-2 comes a new version of Niklaus Wirth's classic work, Algorithms + Data Structure = Programs (PH, l975). It includes new material on sequential structure, searching and priority search trees.

  • Graph Algorithms: Practical Examples in Apache Spark and Neo4j

    This book is a practical guide to getting started with graph algorithms for developers and data scientists who have experience using Apache Spark or Neo4j. You'll walk through hands-on examples that show you how to use graph algorithms in Apache Spark/Neo4j.

  • Parallel Algorithms (Henri Casanova, et al)

    Focusing on algorithms for distributed-memory parallel architectures, the book extracts fundamental ideas and algorithmic principles from the mass of parallel algorithm expertise and practical implementations developed over the last few decades.

  • The Design of Approximation Algorithms (D. P. Williamson)

    This book shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions. is organized around central algorithmic techniques for designing approximation algorithms, including greedy and local search algorithms, dynamic programming, linear and semidefinite programming, and randomization.

  • Algorithms, 4th Edition, by Robert Sedgewick and Kevin Wayne

    It surveys the most important algorithms and data structures in use today. Applications to science, engineering, and industry are a key feature of the book. We motivate each algorithm that we address by examining its impact on specific applications.

  • The Algorithm Design Manual (Steven S. Skiena)

    This book serves as the primary textbook for any algorithm design course while maintaining its status as the premier practical reference guide to algorithms, intended as a manual on algorithm design for both students and computer professionals.

  • Introduction to Design Analysis of Algorithms (K. Raghava Rao)

    This book was very useful to easily understand the algorithms. This book is having enough examples on every algorithm. It has written for the sake of students to provide complete knowledge on Algorithms.

  • Numerical Algorithms: Computer Vision, Machine Learning, etc.

    This book presents a new approach to numerical analysis for modern computer scientists, covers a wide range of topics - from numerical linear algebra to optimization and differential equations - focusing on real-world motivation and unifying themes.

  • Optimization Algorithms- Methods and Applications

    This book covers state-of-the-art optimization methods and their applications in wide range especially for researchers and practitioners who wish to improve their knowledge in this field.

  • Essential Algorithms (Krzysztof Kowalczyk)

    This book provides clear and concise explanation of topics for programmers both starting to learn the Algorithms as well as those diving in more complex topics. Examples are linked to online playground that allows you to play with them.

  • Algorithms and Data Structures: Apps to Graphics and Geometry

    An introductory coverage of algorithms and data structures with application to graphics and geometry aims to show a sample of the intellectual demands required by a computer science curriculum, exercises, many with solutions, are included throughout the book.

  • Notes on Data Structures and Programming Techniques

    There are two purposes to this book: to teach you to program in the C programming language, and to teach you how to choose, implement, and use data structures and standard programming techniques.

  • Global Optimization Algorithms - Theory and Application

    This book is devoted to global optimization algorithms, which are methods to find optimal solutions for given problems. It especially focuses on Evolutionary Computation by discussing evolutionary algorithms, genetic algorithms, Genetic Programming, etc.

  • Data Structures: Fundamental Tools (Wikibooks)

    This book describes data structures from the point of view of programming, with examples, and presents design techniques that allow programmers to develop their own data structures.

  • Algorithms (Panos Louridas)

    An accessible introduction to algorithms, explaining not just what they are but how they work, with examples from a wide range of application areas. It covers three of the most fundamental applications areas: graphs, searching, and sorting.

  • Classic Computer Science Problems in Python (David Kopec)

    This book deepens your knowledge of problem-solving techniques from the realm of computer science by challenging you with time-tested scenarios, exercises, and algorithms. As you work through examples in search, clustering, graphs, and more.

  • Annotated Algorithms in Python: with Applications

    This book covers Analysis and Design of Algorithms, Scientific Computing, Monte Carlo Simulations, and Parallel Algorithms. It teaches the core knowledge required by any scientist interested in numerical algorithms and computational finance.

  • Algorithmic Problem Solving with Python (John B. Schneider)

    This book uses Python to introduce folks to programming and algorithmic thinking. It is sharply focused on classical algorithms, but it also gives a solid understanding of fundamental algorithmic problem-solving techniques.

  • Problem Solving with Algorithms and Data Structures Using Python

    This is a textbook about computer science. It is also about Python. However, there is much more. The tools and techniques that you learn here will be applied over and over as you continue your study of computer science.

  • Swift Algorithms and Data Structures (Wayne Bishop)

    This book blends modern code, illustrations, and computer science to help you build your next app using in the new iOS development language called Swift, provides an introduction to commonly used data structures and algorithms.

  • Ruby Data Structures and Algorithms (Christopher Fox)

    This book is a concise introduction to data structures and algorithms in Ruby. Data structures are presented in a container hierarchy that includes stacks and queues as non-traversable dispensers, and lists, sets, and maps as traversable collections.

  • Data Structures and Algorithm Analysis in C++, 3rd Edition

    A comprehensive treatment focusing on the creation of efficient data structures and algorithms, using C++. This text explains how to select or design the data structure best suited to specific problems.

  • Data Structures and Algorithm Analysis in Java, 3rd Edition

    A comprehensive treatment focusing on the creation of efficient data structures and algorithms, using Java. This text explains how to select or design the data structure best suited to specific problems.

  • Java Structures: Data Structures in Java for the Programmers

    This book attempts to provide a fresh and focused approach to the design and implementation of classic structures in a manner that meshes well with existing Java packages.

  • Clever Algorithms: Nature-Inspired Programming Recipes

    The book describes 45 algorithms from the field of Artificial Intelligence. All algorithm descriptions are complete and consistent to ensure that they are accessible, usable and understandable by a wide audience.

  • Go Data Structures and Algorithms (Christopher Fox)

    A concise introduction to data structures and algorithms in Go. Data structures are presented in a container hierarchy that includes stacks and queues as non-traversable dispensers, and lists, sets, and maps as traversable collections.

  • AI Algorithms, Data Structures, and Idioms in Prolog, Lisp, and Java

    Illustrateing how to program AI algorithms in Lisp, Prolog, and Java. Topics include: simple production-like system based on logic, logic-based learning, and natural language parsing.

  • Advances in Graph Algorithms (Ton Kloks, Yue-Li Wang)

    This is a book about some currently popular topics such as exponential algorithms, fixed-parameter algorithms and algorithms using decomposition trees of graphs which is one of focuses of the book - occupied a whole chapter.

  • Algorithms (Sanjoy Dasgupta, et al)

    This book, 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.

  • Solving NP-Complete Problems (Forbes D. Lewis)

    This is a textbook on heuristic algorithms. From the table of contents: Classes of Problems; Integer Programming; Enumeration Techniques; Dynamic Programming; Approximate Solutions; Local Optimization; Natural Models.

  • An Introduction to the Analysis of Algorithms, 2nd Edition

    The authors have drawn from both classical mathematics and computer science, integrating discrete mathematics, elementary real analysis, combinatorics, algorithms, and data structures.

  • Algorithms in Real Algebraic Geometry (Saugata Basu, et al)

    The monograph gives a self-contained detailed exposition of the algorithmic real algebraic geometry. It will be useful both for beginners and for advanced readers, who work in real algebraic geometry or apply its methods in other fields.

  • Essentials of Metaheuristics (Sean Luke)

    This book is an open set of lecture notes on metaheuristics algorithms. The algorithmic family includes genetic algorithms, hill-climbing, simulated annealing, ant colony optimization, particle swarm optimization, and so on.

  • Data Structures Succinctly, Part 1 (Robert Horvick)

    This is your first step to a better understanding of the different types of data structures, how they behave, and how to interact with them. It also features code samples and vivid diagrams to help you visualize the more abstract structures and algorithms.

  • Data Structures Succinctly, Part 2 (Robert Horvick)

    This is your 2nd step to a better understanding of the different types of data structures, focus on lists, hash tables, heaps, priority queues, AVL trees, and B-trees. It explains what each structure's methods and classes are, the algorithms behind them, etc.

  • Genetic Algorithms in Applications (Rustem Popa)

    This well-organized book takes the reader through the new and rapidly expanding field of genetic algorithms step by step, from a discussion of numerical optimization, to a survey of current extensions to genetic algorithms and applications.

  • Digraphs: Theory, Algorithms and Applications (Bang-Jensen)

    This book is an essential, comprehensive reference of Digraphs for students, and researchers in mathematics, operations research and computer science.

  • Lists, Decisions and Graphs - With an Introduction to Probability

    In this book, four basic areas of discrete mathematics are presented: Counting and Listing (Unit CL), Functions (Unit Fn), Decision Trees and Recursion (Unit DT), and Basic Concepts in Graph Theory (Unit GT).

  • Real-World Applications of Genetic Algorithms (Olympia Roeva)

    This book presents hybrid techniques based on Artificial Neural Network, Fuzzy Sets, Automata Theory, other metaheuristic or classical algorithms, etc. It examines various examples of algorithms in different real-world application domains.

  • Bio-Inspired Computational Algorithms and Their Applications

    Bio-inspired computational algorithms are always topics in artificial intelligence. Integrates contrasting techniques of genetic algorithms, artificial immune systems, particle swarm optimization, and hybrid models to solve many real-world problems.

  • Algorithmic Graph Theory (David Joyner, et al)

    This is an introductory book on algorithmic graph theory. Theory and algorithms are illustrated using the Sage open source mathematics software. It's especially suitable for computer scientists and mathematicians interested in computational complexity.

  • Search Algorithms and Applications (Nashat Mansour)

    Demonstrates the wide applicability of search algorithms for the purpose of developing useful and practical solutions to problems that arise in a variety of problem domains. There are search algorithms that are designed for the prospective quantum computer.

  • Search Algorithms for Engineering Optimization

    This book explores a variety of applications for search methods and techniques in different fields of electrical engineering. By organizing relevant results and applications, this book will serve as a useful resource for students, researchers and practitioners.

  • Evolutionary Algorithms (Eisuke Kita)

    The goal of this book is to provide effective evolutionary algorithms that have been used as an experimental framework within biological evolution and natural selection in the field of artificial life.

  • Algorithms for Modular Elliptic Curves, 2nd Edition (J. E. Cremona)

    This book presents a thorough treatment of many algorithms concerning the arithmetic of elliptic curves, with remarks on computer implementation. An extensive set of tables is provided giving the results of the author's implementation of the algorithms.

  • Matters Computational: Ideas, Algorithms, Source Code

    This book provides algorithms and ideas for computationalists, whether a working programmer or anyone interested in methods of computation. The implementations are done in C++ and the GP language, written for POSIX-compliant platforms.

  • Fast Fourier Transforms (C. Sidney Burrus)

    This book uses an index map, a polynomial decomposition, an operator factorization, and a conversion to a filter to develop a very general and efficient description of fast algorithms to calculate the discrete Fourier transform (DFT).

  • Computational Modeling and Complexity Science: Python Edition

    This book is about complexity science, data structures and algorithms, intermediate programming in Python, and the philosophy of science.

  • Traveling Salesman Problem, Theory and Applications

    This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem, including Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm.

  • Greedy Algorithms (Witold Bednorz)

    This book covers fundamental, theoretical topics as well as advanced, practical applications of Greedy Algorithms.

  • The Art of Computer Programming, Vol. 4 (Donald E. Knuth)

    This is by far the longest chapter in The Art of Computer Programming, a chapter on combinatorial algorithms that will itself fill four full-sized volumes.

  • The Art of Computer Programming, Volumn 1, Fascicle 1: MMIX -- A RISC Computer (Donald E. Knuth)

    This first fascicle updates The Art of Computer Programming, Vol 1, 3rd Edition: Fundamental Algorithms, and ultimately will become part of the 4th edition of that book.

  • Optimization Algorithms on Matrix Manifolds (P.-A. Absil, ...)

    This book offers techniques with broad applications in linear algebra, signal processing, data mining, computer vision, and statistical analysis.

  • Advances in Evolutionary Algorithms (Witold Kosinski)

    Provide effective optimization algorithms for solving a broad class of problems quickly, accurately, and reliably by employing evolutionary mechanisms.

  • Boosting: Foundations and Algorithms (Robert E. Schapire, et al)

    This book, written by the inventors of the Boosting method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds.

  • Data Structures and Algorithms: Reference with Examples

    A key factor of this book and its associated implementations is that all algorithms were designed by authors, using the theory of the algorithm in question as a guideline. It covers the key ideas involved in designing algorithms.

  • Tabu Search (Wassim Jaziri)

    The goal of this book is to report original researches on algorithms and applications of Tabu Search to real-world problems.

  • Algorithmic Game Theory (Noam Nisan, et al)

    This book covers many of the hottest area of useful new game theory research, introducing deep new problems, techniques, etc.

  • Multiagent Systems: Algorithmic, Game-Theoretic, and Logic, etc.

    This comprehensive introduction to a burgeoning field is written from a computer science perspective, while bringing together ideas from operations research, game theory, economics, logic, and even philosophy and linguistics.

  • Planning Algorithms (Steven M. LaValle)

    This is the only book for teaching and referencing of Planning Algorithms in applications including robotics, computational biology, computer graphics, manufacturing, aerospace applications and medicine, etc.

  • Combinatorial Algorithms for Computers and Calculators

    On one level, this is a collection of subroutines, in FORTRAN, for the solution of combinatorial problems.

  • Information Theory, Inference and Learning Algorithms

    This textbook introduces Information theory in tandem with applications, alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction.

  • Algorithms and Complexity (Herbert S. Wilf)

    This is an introductory book on the design and analysis of algorithms. The author uses a careful selection of a few topics to illustrate the tools for algorithm analysis.

  • Problems on Algorithms, 2nd Edition (Ian Parberry)

    This book provides an extensive and varied collection of useful, practical problems on the design, analysis, and verification of algorithms.

  • Algorithms and Data Structures in VLSI Design

    This book presents broad and in-depth coverage of the entire field of modern VLSI Design with ordered binary decision diagrams (OBDDs).

  • Problem Solving for Coding Interviews (Karthik Naidu)

    This book is a software engineer's notes on problem solving. Ideally suited as practice material for coding interviews. This started out as a collection of interview questions and solutions that suthor accumulated over the years.

  • Average Case Analysis of Algorithms on Sequences (Szpankowski)

    It describes methods employed in average case analysis of algorithms, combining both analytical and probabilistic tools in a single volume.

  • Text Algorithms (Maxime Crochemore, et al)

    This much-needed book on the design of algorithms and data structures for text processing emphasizes both theoretical foundations and practical applications. The core is the material on suffix trees and subword graphs, applications of these data structures.

  • Lecture Notes on Bucket Algorithms (Luc Devroye)

    The bucket data structure is rather sensitive to the distribution of the data. In these lecture notes, we attempt to explain the connection between the expected time of various bucket algorithms and the distribution of the data.

  • Sequential and Parallel Sorting Algorithms (Hans Werner Lang)

    Varieties of sequential and parallel sorting algorithms are explained, followed by source code (in Java and C++). Descriptions are brief and intuitive, with just enough theory thrown in to make you nervous.

  • Knapsack Problems: Algorithms and Computer Implementations

    The text fully develops an algorithmic approach to Knapsack Problems without losing mathematical rigor. It provides a comprehensive overview of the methods for solving Knapsack Problems (KP), its variants and generalizations.

  • Discrete Mathematics with Algorithms (M. O. Albertson, et al)

    This is a first-year course in discrete mathematics requireing no calculus or computer programming experience.

  • Art Gallery Theorems and Algorithms (Joseph O'Rourke)

    This book is the most comprehensive collection of results on polygons currently available and thus earns its place as a standard text in discrete and computational geometry. It is very well written and a pleasure to read.

  • Data Structures and Algorithms with OPP Design Patterns in Java

    It promotes object-oriented design using Java and illustrates the use of the latest object-oriented design patterns. Virtually all the data structures are discussed in the context of a single class hierarchy.

  • Data Structures and Algorithms with OPP Design Patterns in C++

    This book presents readers with a modern, object-oriented perspective for looking at data structures and algorithms using C++, clearly showing how to use polymorphism and inheritance, and including fragments from working and tested programs.

  • Data Structures and Algorithms with OPP Design Patterns in C#

    It promotes object-oriented design using C# and illustrates the use of the latest object-oriented design patterns. Virtually all the data structures are discussed in the context of a single class hierarchy.

  • Data Structures and Algorithms with OPP Design Patterns in Ruby

    It promotes object-oriented design using Ruby and illustrates the use of the latest object-oriented design patterns. Virtually all the data structures are discussed in the context of a single class hierarchy.

  • Data Structures and Algorithms with OPP Design Patterns in Python

    It promotes object-oriented design using Python and illustrates the use of the latest object-oriented design patterns. Virtually all the data structures are discussed in the context of a single class hierarchy.

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