In Algorithms Illuminated, Tim Roughgarden teaches the basics of algorithms in the most accessible way imaginable. This Omnibus Edition contains the complete text of Parts 1-4, with thorough coverage of asymptotic analysis, graph search and shortest paths, data structures, divide-and-conquer algorithms, greedy algorithms, dynamic programming, and NP-hard problems. Hundreds of worked examples, quizzes, and exercises, plus comprehensive online videos, help readers become better programmers; sharpen their analytical skills; learn to think algorithmically; acquire literacy with computer science's greatest hits; and ace their technical interviews.

Algorithms are the heart and soul of computer science. Their applications range from network routing and computational genomics to public-key cryptography and machine learning. Studying algorithms can make you a better programmer, a clearer thinker, and a master of technical interviews. Algorithms Illuminated is an accessible introduction to the subject for anyone with at least a little programming experience. The exposition emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details---like a transcript of what an expert algorithms tutor would say over a series of one-on-one lessons. Part 1 covers asymptotic analysis and big-O notation, divide-and-conquer algorithms and the master method, randomized algorithms, and several famous algorithms for sorting and selection.

Algorithms are the heart and soul of computer science. Their applications range from network routing and computational genomics to public-key cryptography and machine learning. Studying algorithms can make you a better programmer, a clearer thinker, and a master of technical interviews. Algorithms Illuminated is an accessible introduction to the subject for anyone with at least a little programming experience. The exposition emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details---like a transcript of what an expert algorithms tutor would say over a series of one-on-one lessons. Part 2 covers graph search and applications, shortest paths, and the usage and implementation of several data structures (heaps, search trees, hash tables, and bloom filters).

Algorithms are the heart and soul of computer science. Their applications range from network routing and computational genomics to public-key cryptography and machine learning. Studying algorithms can make you a better programmer, a clearer thinker, and a master of technical interviews. Algorithms Illuminated is an accessible introduction to the subject for anyone with at least a little programming experience. The exposition emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details---like a transcript of what an expert algorithms tutor would say over a series of one-on-one lessons. Part 3 covers greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, shortest paths, optimal search trees).

Fourth book in a series that provides an accessible, no-nonsense, and programming language-agnostic introduction to algorithms. Includes hints of solutions to all quizzes and problems, and a series of YouTube videos by the author accompanies the book. Part 4 covers algorithmic tools for tackling NP-hard problems (heuristic algorithms, local search, dynamic programming, MIP and SAT solvers) and techniques for quickly recognizing NP-hard problems in the wild.

Computer science and economics have engaged in a lively interaction over the past fifteen years, resulting in the new field of algorithmic game theory. Many problems that are central to modern computer science, ranging from resource allocation in large networks to online advertising, involve interactions between multiple self-interested parties. Economics and game theory offer a host of useful models and definitions to reason about such problems. The flow of ideas also travels in the other direction, and concepts from computer science are increasingly important in economics. This book grew out of the author's Stanford University course on algorithmic game theory, and aims to give students and other newcomers a quick and accessible introduction to many of the most important concepts in the field. The book also includes case studies on online advertising, wireless spectrum auctions, kidney exchange, and network management.

Los algoritmos son el corazón y el alma de la informática. Se aplican a ámbitos tan diversos como el diseño de redes, la genética computacional, el cifrado con clave pública o la implementación de sistemas de bases de datos. El estudio de los algoritmos te convertirá en un mejor programador, hará que pienses con más claridad y será una ayuda indispensable para tus entrevistas de trabajo.

Algoritmos iluminados es una introducción sencilla a la materia, escrito como una transcripción de lo que te explicaría un tutor experto en algoritmos en una lección personalizada.

La cuarta parte cubre las herramientas algorítmicas necesarias para tratar con problemas NP-complejos (algoritmos heurísticos, búsqueda local, programación dinámica, MIP y SAT) y técnicas para reconocer rápidamente cuándo un problema es NP-complejo.

Tim Roughgarden es profesor de ciencias de la computación en la Universidad de Columbia. Es experto en diseño, análisis, aplicaciones y limitaciones de algoritmos. Esta serie de cuatro libros se inspira en los cursos sobre algoritmia que ha impartido en línea, regularmente, desde 2012.

Los algoritmos son el corazón y el alma de la informática. Se aplican a ámbitos tan diversos como el diseño de redes, la genética computacional, el cifrado con clave pública o la implementación de sistemas de bases de datos. El estudio de los algoritmos te convertirá en un mejor programador, hará que pienses con más claridad y será una ayuda indispensable para tus entrevistas de trabajo.

Algoritmos iluminados es una introducción sencilla a la materia, escrito como una transcripción de lo que te explicaría un tutor experto en algoritmos en una lección personalizada.

La primera parte cubre el análisis asintótico y la notación Big-O, algoritmos de divide y vencerás y el método maestro, algoritmos aleatorizados y varias técnicas muy conocidas, aplicables a la ordenación y selección.

Tim Roughgarden es profesor de ciencias de la computación en la Universidad de Columbia. Es experto en diseño, análisis, aplicaciones y limitaciones de algoritmos. Esta serie de cuatro libros se inspira en los cursos sobre algoritmia que ha impartido en línea, regularmente, desde 2012.

This monograph comprises a series of ten lectures divided into two parts. Part 1, referred to as the Solar Lectures, focuses on the communication and computational complexity of computing an (approximate) Nash equilibrium. Part 2, the Lunar Lectures, focuses on applications of computational complexity theory to game theory and economics. The goal of this short-course is twofold: (i) to explain how complexity theory has helped illuminate several barriers in economics and game theory, and (ii) to illustrate how game-theoretic questions have led to new and interesting complexity theory, including several very recent breakthroughs. While the solar lectures build on each other to some extent, the lunar lectures are episodic and can be read independently of each other. Most of the lunar lectures have the flavor of applied complexity theory and are less technically intense. Written in a relaxed style, the author uses his didactic expertise to guide the reader through the theory in an insightful and enjoyable manner. No background in game theory is assumed, making the whole text informative and accessible to a wide audience. This monograph gives the reader an excellent introduction to the basics of the subject and highlights some of the most recent breakthroughs in research. It provides the reader with a launch pad for further research.

Computer science and economics have engaged in a lively interaction over the past fifteen years, resulting in the new field of algorithmic game theory. Many problems that are central to modern computer science, ranging from resource allocation in large networks to online advertising, involve interactions between multiple self-interested parties. Economics and game theory offer a host of useful models and definitions to reason about such problems. The flow of ideas also travels in the other direction, and concepts from computer science are increasingly important in economics. This book grew out of the author's Stanford University course on algorithmic game theory, and aims to give students and other newcomers a quick and accessible introduction to many of the most important concepts in the field. The book also includes case studies on online advertising, wireless spectrum auctions, kidney exchange, and network management.

An analysis of the loss in performance caused by selfish, uncoordinated behavior in networks.

Most of us prefer to commute by the shortest route available, without taking into account the traffic congestion that we cause for others. Many networks, including computer networks, suffer from some type of this selfish routing. In Selfish Routing and the Price of Anarchy, Tim Roughgarden studies the loss of social welfare caused by selfish, uncoordinated behavior in networks. He quantifies the price of anarchy--the worst-possible loss of social welfare from selfish routing--and also discusses several methods for improving the price of anarchy with centralized control.

Roughgarden begins with a relatively nontechnical introduction to selfish routing, describing two important examples that motivate the problems that follow. The first, Pigou's Example, demonstrates that selfish behavior need not generate a socially optimal outcome. The second, the counterintiuitve Braess's Paradox, shows that network improvements can degrade network performance. He then develops techniques for quantifying the price of anarchy (with Pigou's Example playing a central role). Next, he analyzes Braess's Paradox and the computational complexity of detecting it algorithmically, and he describes Stackelberg routing, which improves the price of anarchy using a modest degree of central control. Finally, he defines several open problems that may inspire further research. Roughgarden's work will be of interest not only to researchers and graduate students in theoretical computer science and optimization but also to other computer scientists, as well as to economists, electrical engineers, and mathematicians.

Los algoritmos son el corazón y el alma de la informática. Se aplican a ámbitos tan diversos como el diseño de redes, la genética computacional, el cifrado con clave pública o la implementación de sistemas de bases de datos. El estudio de los algoritmos te convertirá en un mejor programador, hará que pienses con más claridad y será una ayuda indispensable para tus entrevistas de trabajo.

Algoritmos iluminados es una introducción sencilla a la materia, escrito como una transcripción de lo que te explicaría un tutor experto en algoritmos en una lección personalizada.

La tercera parte cubre algoritmos voraces (planificación, árboles de expansión mínimos, agrupamiento, códigos de Huffman) y la programación dinámica (mochila, alineamiento de secuencias, caminos más cortos, árboles de búsqueda óptimos).

Tim Roughgarden es profesor de ciencias de la computación en la Universidad de Columbia. Es experto en diseño, análisis, aplicaciones y limitaciones de algoritmos. Esta serie de cuatro libros se inspira en los cursos sobre algoritmia que ha impartido en línea, regularmente, desde 2012.

Communication Complexity (for Algorithm Designers) collects the lecture notes from the author's eponymous course taught at Stanford in the winter quarter of 2015. The two primary goals of the text are: (1) Learn several canonical problems in communication complexity that are useful for proving lower bounds for algorithms (Disjointness, Index, Gap-Hamming, and so on). (2) Learn how to reduce lower bounds for fundamental algorithmic problems to communication complexity lower bounds. Along the way, readers will also get exposure to a lot of cool computational models and some famous results about them - data streams and linear sketches, compressive sensing, space-query time trade-offs in data structures, sublinear-time algorithms, and the extension complexity of linear programs. We also scratch the surface of techniques for proving communication complexity lower bounds (fooling sets, corruption arguments, and so on). Readers are assumed to be familiar with undergraduate-level algorithms, as well as the statements of standard large deviation inequalities (Markov, Chebyshev, and Chernoff- Hoeffding).