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"Optimization algorithms" as a book category covers methods and theory for finding best or near-best solutions to mathematical, engineering, and real-world decision problems. Books in this category explain models (linear, nonlinear, convex, integer, combinatorial), algorithmic approaches (gradient and Newton methods, interior-point and simplex methods, branch-and-bound, dynamic programming, greedy algorithms, and metaheuristics such as genetic algorithms, simulated annealing, and particle swarm), convergence and complexity analysis, and practical implementation issues (numerical stability, scaling, and software). They also discuss applications across machine learning, control, logistics, finance, network design, and scientific computing, often illustrating model formulation, benchmarking, and case studies. Intended readers range from undergraduates and graduate students to researchers and industry practitioners, with texts varying from introductory to advanced and from theoretical to application-focused.

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