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.

This book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first in

This book presents tools and methods for large-scale and distributed optimization. Since many methods in "Big Data" fields rely on solving large-scale optimizat

Surveys the theory and history of the alternating direction method of multipliers, and discusses its applications to a wide variety of statistical and machine l

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