Non-convex Optimization for Machine Learning takes an in-depth look at the basics of non-convex optimization with applications to machine learning. It introduce

This monograph presents the main complexity theorems in convex optimization and their corresponding algorithms. It begins with the fundamental theory of black-b

This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms.

An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay betw

This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problem