Description
Optimization Models provides an accessible introduction to mathematical optimization, with particular emphasis on convex optimization and its practical applications. The book begins with fundamental linear algebra, covering vectors and functions, matrices, symmetric matrices, singular value decomposition, linear equations and least-squares problems, and matrix algorithms. It then develops convex optimization models, including linear, quadratic, geometric, second-order cone, robust, and semidefinite models, followed by algorithms for smooth and nonsmooth convex optimization. The final section applies these techniques to learning from data, computational finance, control problems, and engineering design, including applications in portfolio optimization, classification, digital filter design, aircraft design, and supply chain management. Through mathematical foundations, optimization models, algorithms, and practical applications, the book provides a comprehensive introduction to optimization for students and practitioners in mathematics, engineering, computer science, and related fields.
