Covering the current state of the art, this book explores an important and central issue in convex optimization: optimality conditions. Although many results presented in the chapters can also be proved in infinite dimensions, the text focuses on finite dimensions to allow for much deeper results and a better understanding of the structures involved in a convex optimization problem. The authors include examples wherever needed, details of major results, and proofs of the main results.

Anulekha Dhara earned her Ph.d. in IIT Delhi and subsequently moved to IIT Kanpur for her post-doctoral studies. Currently, she is a post-doctoral fellow in Mathematics at the University of Avignon, France. Her main area of interest is optimization theory.

Joydeep Dutta is an Associate Professor of Mathematics at the Indian Institute of Technology, (IIT) Kanpur. His main area of interest is optimization theory and applications.

What Is Convex Optimization?. Tools for Convex Optimization. Basic Optimality Conditions using the Normal Cone. Saddle Points, Optimality and Duality. Enhanced Fritz John Optimality Conditions. Optimality without Constraint Qualification. Sequential Optimality Conditions and Generalized Constraint Qualification. Representation of the Feasible Set and KKT Conditions. Weak Sharp Minima in Convex Optimization. Approximate Optimality Conditions. Convex Semi-infinite Optimization. Convexity in Non-Convex Optimization. Bibliography. Index.