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Computational complexity books explore the fundamental limits of computation by analyzing how much time, memory, randomness, or other resources are required to solve problems within formal models of computation. This category covers the definition and relationships of complexity classes (e.g., P, NP, coNP, NP-complete, PSPACE, BPP), reductions and completeness, lower bounds, circuit and communication complexity, randomness and derandomization, approximation and parameterized complexity, and connections to cryptography, learning theory, optimization, and the famed P vs NP question. Titles range from accessible overviews and textbooks to advanced monographs and surveys, serving students, researchers, and practitioners who want a rigorous understanding of what can and cannot be computed efficiently.

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