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 Alexander Shen - arXiv.org
Algorithmic information theory studies description complexity and randomness. This text covers the basic notions of algorithmic information theory: Kolmogorov complexity, Solomonoff universal a priori probability, effective Hausdorff dimension, etc.
by Oded Goldreich - Cambridge University Press
The book gives the mathematical underpinnings for cryptography; this includes one-way functions, pseudorandom generators, and zero-knowledge proofs. Throughout, definitions are complete and detailed; proofs are rigorous and given in full.
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 Oded Goldreich
Complexity theory is the study of the intrinsic complexity of computational tasks. The book is aimed at exposing the students to the basic results and research directions in the field. The focus was on concepts, complex technical proofs were avoided.