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"Constrained optimization" as a book category covers the theory, methods, and applications of finding the best feasible solutions to problems subject to restrictions. It includes texts on linear and nonlinear programming, convex and nonconvex problems, integer and combinatorial optimization, and special topics such as stochastic, robust, and multiobjective optimization; core methods include Lagrange multipliers, Karush–Kuhn–Tucker conditions, duality, interior‑point and active‑set algorithms, sequential quadratic programming, and branch‑and‑bound. Books in this category range from rigorous mathematical treatments and algorithmic analysis to practical guides and case studies in engineering design, operations research, economics, finance, control systems, and machine learning, and typically address modeling constraints (equality/inequality, discrete/continuous), sensitivity and stability, and computational tools for implementation. Intended audiences vary from advanced undergraduates to researchers and practitioners looking to model constrained decision problems and choose or implement appropriate solution techniques.

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