Think Complexity: Complexity Science and Computational Modeling
by Allen B. Downey
Publisher: Green Tea Press 2012
Number of pages: 146
This book is about complexity science, data structures and algorithms, intermediate programming in Python, and the philosophy of science. The book focuses on discrete models, which include graphs, cellular automata, and agent-based models. They are often characterized by structure, rules and transitions rather than by equations.
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by Sanjeev Arora, Boaz Barak - Cambridge University Press
The book provides an introduction to basic complexity classes, lower bounds on resources required to solve tasks on concrete models such as decision trees or circuits, derandomization and pseudorandomness, proof complexity, quantum computing, etc.
by Allen Downey - Green Tea Press
This book is about data structures and algorithms, intermediate programming in Python, complexity science and the philosophy of science. The book covers Graphs, Analysis of algorithms, Scale-free networks, Cellular Automata, Agent-based models, etc.
by Tim Roughgarden - Stanford University
The two biggest goals of the course are: 1. Learn several canonical problems that have proved the most useful for proving lower bounds; 2. Learn how to reduce lower bounds for fundamental algorithmic problems to communication complexity lower bounds.
by Luca Trevisan
Notes from a graduate courses on Computational Complexity. The first 15 lectures cover fundamentals, the remaining is advanced material: Hastad's optimal inapproximability results, lower bounds for parity in bounded depth-circuits, and more.