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"Trust-region methods" as a book category would encompass texts focused on a class of numerical optimization algorithms that build and iteratively minimize local models of an objective function within a neighborhood (the "trust region") whose size is adapted according to how well the model predicts actual progress. Books in this category typically cover mathematical foundations (Taylor models, model selection, convergence theory and rates), algorithmic frameworks (exact and inexact subproblem solvers, dogleg and truncated-Newton approaches, quasi-Newton and limited-memory variants), practical implementation issues (trust-region radius control, preconditioning, handling indefinite Hessians, stopping criteria), extensions to constrained and nonconvex problems (trust-region reflective methods, sequential quadratic programming hybrids), and derivative-free and large-scale adaptations. They also discuss numerical analysis, performance benchmarking, and applications across engineering, inverse problems, PDE-constrained optimization, and machine learning, making the category suitable for advanced undergraduates, graduate students, researchers, and practitioners seeking both theoretical depth and implementation guidance.

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