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Twenty Lectures on Algorithmic Game Theory
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.
Selfish Routing and the Price of Anarchy
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...
Beyond the Worst-Case Analysis of Algorithms
Introduces exciting new methods for assessing algorithms for problems ranging from clustering to linear programming to neural networks.
Algorithms Illuminated, Part 1
Algorithms Illuminated is an accessible introduction to algorithms for anyone with at least a little programming experience, based on a sequence of popular online courses. Part 1 covers asymptotic analysis and big-O notation, divide-and-conquer algorithms, randomized algorithms, and several famous algorithms for sorting and selection.
Communication Complexity (for Algorithm Designers)
This book deals mostly with impossibility results - lower bounds on what can be accomplished by algorithms. However, the perspective is unapologetically that of an algorithm designer. The reader will learn lower bound technology on a "need-to-know" basis, guided by fundamental algorithmic problems that we care about.
Algorithms Illuminated (Part 3)
Accessible, no-nonsense, and programming language-agnostic introduction to algorithms. Part 3 covers greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, shortest paths, optimal search trees).
Complexity Theory, Game Theory, and Economics
This monograph comprises a series of ten lectures divided into two parts. Part 1 focuses on the communication and computational complexity of computing an (approximate) Nash equilibrium. Part 2 focuses on applications of computational complexity theory to game theory and economics.
Beyond the Worst-Case Analysis of Algorithms
There are no silver bullets in algorithm design, and no single algorithmic idea is powerful and flexible enough to solve every computational problem. Nor are there silver bullets in algorithm analysis, as the most enlightening method for analyzing an algorithm often depends on the problem and the application. However, typical algorithms courses rely almost entirely on a single analysis framework, that of worst-case analysis, wherein an algorithm is assessed by its worst performance on any input of a given size. The purpose of this book is to popularize several alternatives to worst-case analysis and their most notable algorithmic applications, from clustering to linear programming to neural network training. Forty leading researchers have contributed introductions to different facets of this field, emphasizing the most important models and results, many of which can be taught in lectures to beginning graduate students in theoretical computer science and machine learning.
Algorithms Illuminated
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.
