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This textbook offers a mathematically rigorous introduction to convex optimization, blending classical theory with modern topics. Its elementary treatment based on linear algebra, calculus, and real analysis, focus on mathematical foundations, conversational tone, and over 170 exercises make this one of the most accessible textbooks on the topic.
List of contents
Preface; Main notational conventions; Part I. Convex Sets in Rn: From First Acquaintance to Linear Programming Duality: 1. First acquaintance with convex sets; 2. Theorems of caratheodory, radon, and helly; 3. Polyhedral representations and Fourier-Motzkin elimination; 4. General theorem on alternative and linear programming duality; 5. Exercises for Part I; Part II. Separation Theorem, Extreme Points, Recessive Directions, and Geometry of Polyhedral Sets: 6. Separation theorem and geometry of convex sets; 7. Geometry of polyhedral sets; 8. Exercises for Part II; Part III. Convex Functions: 9. First acquaintance with convex functions; 10. How to detect convexity; 11. Minima and maxima of convex functions; 12. Subgradients; 13. Legendre transform; 14. Functions of eigenvalues of symmetric matrices; 15. Exercises for Part III; Part IV. Convex Programming, Lagrange Duality, Saddle Points: 16. Convex programming problems and convex theorem on alternative; 17. Lagrange function and Lagrange duality; 18. Convex programming in cone-constrained form; 19. Optimality conditions in convex programming; 20. Cone-convex functions: elementary calculus and examples; 21. Mathematical programming optimality conditions; 22. Saddle points; 23. Exercises for Part IV; Appendices.
About the author
Fatma Kılınç-Karzan is a Professor of Operations Research at Tepper School of Business, Carnegie Mellon University. She was awarded the 2015 INFORMS Optimization Society Prize for Young Researchers, the 2014 INFORMS JFIG Best Paper Award (with S. Yıldız), and an NSF CAREER Award in 2015. Her research focuses on foundational theory and algorithms for convex optimization and structured nonconvex optimization and their applications in optimization under uncertainty, machine learning, and business analytics. She has been an elected member on the councils of the Mathematical Optimization Society and INFORMS Computing Society, and has served on the editorial boards of MOS-SIAM Book Series on Optimization, MathProgA, MathOR, OPRE, SIAM J Opt, IJOC, and OMS.Arkadi Nemirovski is the John P. Hunter, Jr. Chair and Professor of Industrial and Systems Engineering at Georgia Tech. He has co-authored six optimization textbooks including this one, and he has received many rewards for his contributions to the field, including the 1982 MPS-SIAM Fulkerson Prize (with L. Khachiyan and D. Yudin), the 1991 MPS-SIAM Dantzig Prize (with M. Grotschel), the 2003 INFORMS John von Neumann Theory Prize (with M. Todd), the 2019 SIAM Norbert Wiener Prize (with M. Berger), and the 2023 WLA Prize (with Yu. Nesterov). His research focuses on convex optimization (algorithmic design and complexity analysis, optimization under uncertainty, engineering applications) and nonparametric statistics. He is a member of the National Academy of Engineering, the American Academy of Arts and Sciences, and National Academy of Sciences.
