... Functions, pp. 91–111. Springer New York, New York, NY (2012). DOI 10.1007/978-1-4614-4340-7_4 Sopasakis, P., Freris, N., Patrinos, P.: Accelerated reconstruction of a ... Forward-Backward Splitting via an Inexact Newton Method 411.

... algorithms for nonconvex optimization. In: ICML Workshop: Modern Trends in Nonconvex Optimization for Machine ... Forward-backward envelope for the sum of two nonconvex functions: Further properties and nonmonotone line-search ...

Learning with Submodular Functions presents the theory of submodular functions in a self-contained way from a convex analysis perspective, presenting tight links between certain polyhedra, combinatorial optimization and convex optimization ...

The primary goal of this book is to provide a self-contained, comprehensive study of the main ?rst-order methods that are frequently used in solving large-scale problems.

The contributions in this volume were written by eminent experts in the field, each surveying the original contributions that created the field, and then providing an up-to-date review of the latest contributions.

The technique of reduced rank regression and its encompassing features are the primary focus of this book. The book develops the method of reduced-rank regression starting from the classical multivariate linear regression model.

Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research.

This book presents tools and methods for large-scale and distributed optimization.

This book incorporates diverse perspectives from broad-ranging areas of research including, variational analysis, numerical linear algebra, biotechnology, materials science, computational solid-state physics, and chemistry.