The book offers a comprehensive introduction to computational fluid dynamics and heat transfer, tailored for graduate students. It begins with essential mathematical concepts and progresses through common problems in the field, ensuring a solid understanding. Each chapter includes numerous end-of-chapter problems to reinforce learning and assess comprehension, making it an effective resource for both theoretical understanding and practical application.
The book presents ten lectures organized into two parts: the Solar Lectures and the Lunar Lectures. The first part explores the communication and computational complexity involved in finding an approximate Nash equilibrium, while the second part applies complexity theory to game theory and economics. It aims to highlight how complexity theory reveals challenges in these fields and demonstrates how questions from game theory have inspired advancements in complexity theory, including recent breakthroughs.
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).
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 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.
Focusing on communication complexity, this text presents lecture notes from a Stanford course, emphasizing key problems like Disjointness and Gap-Hamming. It aims to equip readers with the tools to understand and prove lower bounds for algorithms, showcasing the connection between algorithmic challenges and communication complexity. This resource is particularly valuable for algorithm designers seeking to deepen their knowledge in this specialized area.
This accessible introduction features case studies in online advertising, spectrum auctions, kidney exchange, and network management.