This book aims at an up-to-date and accessible development of algorithms for solving convex optimization problems. The book covers almost all the major classes of convex optimization algorithms. Principal among these are gradient, subgradient, polyhedral approximation, proximal, and interior point methods. Most of these methods rely on convexity (but not necessarily differentiability) in the cost and constraint functions, and are often connected in various ways to duality. The book contains numerous examples describing in detail applications to specially structured problems. The book complements our Convex Optimization Theory (Athena Scientific, 2009) book, but can be read independently. The latter book focuses on convexity theory and optimization duality, while the present book focuses on algorithmic issues. The two books share mathematical prerequisites, notation, and style, and together cover the entire finite-dimensional convex optimization field. Both books rely on rigorous mathematical analysis, but also aim at an intuitive exposition that makes use of visualization where possible. This is facilitated by the extensive use of analytical and algorithmic concepts of duality, which by nature lend themselves to geometrical interpretation.
- ISBN: 9781886529281
- Editorial: ATHENA SCIENTIFIC
- Encuadernacion: Tela
- Páginas: 576
- Fecha Publicación: 01/02/2015
- Nº Volúmenes: 1
- Idioma: