Bücher von Tim Roughgarden

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.

Presents 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.

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).

The two primary goals of the text are to learn several canonical problems in communication complexity that are useful for proving lower bounds for algorithms (Disjointness, Index, Gap-Hamming, and so on); and to learn how to reduce lower bounds for fundamental algorithmic problems to communication complexity lower bounds.