Proximal Algorithms discusses proximal operators and proximal algorithms, and illustrates their applicability to standard and distributed convex optimization in general and many applications of recent interest in particular.
... proximal gradient algorithm for decentralized composite optimization. IEEE Trans. Signal Process. 63(22), 6013–6023 ... primal-dual strategy for composite opti- mization over distributed features, inProceedings of the 2020 28th ...
Surveys the theory and history of the alternating direction method of multipliers, and discusses its applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic ...
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.