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"Algorithm optimization" as a book category covers methods and principles for making algorithms faster, use less memory, and run more efficiently in real-world settings. Titles in this genre range from theoretical treatments of asymptotic complexity and lower/upper bounds to practical guides on profiling, data-structure selection, cache-friendly and branch-predictable coding, parallelization, approximation and heuristic techniques, and hardware- or domain-specific tuning (e.g., GPUs, distributed systems, numerical computing). Books typically combine formal analysis, proofs, worked examples, performance benchmarks, and implementation advice, and are aimed at students, researchers, and software engineers who need to design, analyze, or improve algorithms for performance-critical applications.

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