Solving NP-Complete Problems
by F. D. Lewis
Publisher: University of Kentucky 2013
Number of pages: 123
This is an on-line textbook on heuristic algorithms. From the table of contents: Classes of Problems; Integer Programming; Enumeration Techniques; Dynamic Programming; Approximate Solutions; Local Optimization; Natural Models.
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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 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 Johan Håstad
This set of notes gives the broad picture of modern complexity theory, defines the basic complexity classes, gives some examples of each complexity class and proves the most standard relations. The author emphasizes the ideas involved in the proofs.
by Oded Goldreich - Cambridge University Press
This book offers a comprehensive perspective to modern topics in complexity theory. It can be used as an introduction as either a textbook or for self-study, or to experts, since it provides expositions of the various sub-areas of complexity theory.