Algorithmic Randomness and Complexity
by R. G. Downey, D. R. Hirschfeldt
Publisher: Springer 2010
Number of pages: 629
Computability and complexity theory are two central areas of research in theoretical computer science. This book provides a systematic, technical development of algorithmic randomness and complexity for scientists from diverse fields.
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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 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.
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.
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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.