Convex Optimization by Stephen BoydTotal variation image in- painting. This book provides a comprehensive introduction to the subject, and shows in detail how such problems can be solved numerically with great efficiency. Covered in this book, along with exercises that allow the students to. Convex optimization is a very important area in Machine learning as convex functions have very nice properties local minima is global minima. And then there' s the Golub and van Loan book Matrix Computations. Hello, I have a doubt can anyone help?
Best Books on Convex Optimization
Convex Optimization – Boyd and Vandenberghe
The book will be accessible not only to mathematicians but also to researchers and students who want to use convex optimization in applied fields like engineering, computer science, economics, statistics, or others. I recommend it as one of the best optimization textbooks that have appeared in the last years. 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. Convex optimization problems arise frequently in many different fields.
Convex optimization is a collection of techniques for optimizing a small but interesting class of functions. Examples of convex optimization problems are least squares regression and linear programming. It's a very interesting subject, but since most optimization problems data scientists are interested in aren't convex, it may not offer the same practical utility as more applied subjects. This is the book that everyone we know has used as an introduction to convex optimization, and it's easy to understand why. It's very coherently written, has good coverage of the basic mathematics, applications and algorithms, and it doesn't really feel much harder than linear algebra or calculus. We especially found the section on statistical estimation useful.