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Interior-point methods is a technical nonfiction category covering mathematical foundations, algorithms, and practical implementations of optimization techniques that traverse the interior of feasible regions to solve linear, nonlinear, convex, and some nonconvex problems; books in this category range from introductory treatments explaining barrier functions, Newton and primal-dual path-following methods, and complexity analysis, to advanced monographs on semidefinite programming, interior-point theory, and large-scale numerical implementation and software. Typical volumes present rigorous proofs, algorithmic design, convergence and polynomial-time results, numerical stability and tuning, and case studies from operations research, control, machine learning, and engineering. They are aimed at graduate students, researchers, and practitioners with a background in linear algebra, convex analysis, and numerical optimization, and often include exercises, code examples, and guidance for adapting methods to real-world problem structure.

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