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.

"It discusses a number of major approaches to the subject, bringing together many results from the past thirty-five years into one handy volume. … Researchers in variational analysis should find this book to be a useful reference; for those new to convex optimization, it provides a very accessible entry point to the field. I have begun recommending it to graduate students who would like to learn about convex subdifferential calculus. … a valuable book, a most welcome addition to the optimization theory literature."—Doug Ward, Mathematical Reviews, January 2013

• 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.

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.

"It discusses a number of major approaches to the subject, bringing together many results from the past thirty-five years into one handy volume. … Researchers in variational analysis should find this book to be a useful reference; for those new to convex optimization, it provides a very accessible entry point to the field. I have begun recommending it to graduate students who would like to learn about convex subdifferential calculus. … a valuable book, a most welcome addition to the optimization theory literature."—Doug Ward, Mathematical Reviews, January 2013

• 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.

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.

"It discusses a number of major approaches to the subject, bringing together many results from the past thirty-five years into one handy volume. … Researchers in variational analysis should find this book to be a useful reference; for those new to convex optimization, it provides a very accessible entry point to the field. I have begun recommending it to graduate students who would like to learn about convex subdifferential calculus. … a valuable book, a most welcome addition to the optimization theory literature."—Doug Ward, Mathematical Reviews, January 2013

• 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.