Description
Book SynopsisThe focus of this book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. It contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance and economics.
Trade Review'Boyd and Vandenberghe have written a beautiful book that I strongly recommend to everyone interested in optimization and computational mathematics: Convex Optimization is a very readable introduction to this modern field of research.' Mathematics of Operations Research
'… a beautiful book that I strongly recommend to everyone interested in optimization and computational mathematics … a very readable and inspiring introduction to this modern field of research. I recommend it as one of the best optimization textbooks that have appeared in the last years.' Mathematical Methods of Operations Research
'I highly recommend it either if you teach nonlinear optimization at the graduate level for a supplementary reading list and for your library, or if you solve optimization problems and wish to know more about solution methods and applications.' International Statistical institute
'… the whole book is characterized by clarity. … a very good pedagogical book … excellent to grasp the important concepts of convex analysis [and] to develop an art in modelling optimization problems intelligently.' Matapli
'The book by Boyd and Vandenberghe reviewed here is one of … the best I have ever seen … it is a gentle, but rigorous, introduction to the basic concepts and methods of the field … this book is meant to be a 'first book' for the student or practitioner of optimization. However, I think that even the experienced researcher in the field has something to gain from reading this book: I have very much enjoyed the easy to follow presentation of many meaningful examples and suggestive interpretations meant to help the student's understanding penetrate beyond the surface of the formal description of the concepts and techniques. For teachers of convex optimization this book can be a gold mine of exercises. MathSciNet
Table of ContentsPreface; 1. Introduction; Part I. Theory: 2. Convex sets; 3. Convex functions; 4. Convex optimization problems; 5. Duality; Part II. Applications: 6. Approximation and fitting; 7. Statistical estimation; 8. Geometrical problems; Part III. Algorithms: 9. Unconstrained minimization; 10. Equality constrained minimization; 11. Interior-point methods; Appendices.
