Current computational optimization is a rich and thriving mathematical discipline, and its underlying theory grows ever more sophisticated. The powerful and elegant language of convex analysis unifies much of this theory. The aim of this book is to provide a concise, accessible account of convex analysis and its applications and extensions, for a broad audience. It can serve as a teaching text, at roughly the level of first-year graduate students. The new edition adds material on semismooth optimization, as well as several new proofs that will make this book even more self-contained.

This book provides a concise, accessible account of convex analysis and its applications and extensions, for a broad audience. It can serve as a teaching text, at roughly the level of first year graduate students, since the main body of the text is self-contained, with each section rounded off by an often extensive set of optional exercises. The new edition adds material on semismooth optimization, as well as several new proofs that will make this book even more self-contained.

Background.- Inequality constraints.- Fenchel duality.- Convex analysis.- Special cases.- Nonsmooth optimization.- The Karush-Kuhn-Tucker Theorem.- Fixed points.- Postscript: infinite versus finite dimensions.- List of results and notation.