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Nonlinear Programming as a book category covers the theory, methods, and applications of optimization problems in which the objective function or the constraints (or both) are nonlinear. Titles in this category range from introductory textbooks that build the necessary mathematical foundations (calculus, linear algebra, convexity) to advanced monographs on duality theory, Karush–Kuhn–Tucker conditions, and convergence analysis, as well as practical guides focused on algorithmic approaches such as gradient methods, Newton and quasi-Newton methods, sequential quadratic programming, interior-point methods, and global optimization techniques. Books often include modeling strategies, numerical implementation, and use of software libraries (e.g., IPOPT, KNITRO, SNOPT) and discuss applications across engineering design, economics, machine learning, control, and operations research. The audience spans advanced undergraduates and graduate students, researchers seeking theoretical depth, and practitioners needing robust numerical methods for real-world nonlinear problems.

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