V-Invex Functions and Vector Optimization: Springer Optimization and Its Applications, cartea 14

General Introduction.- V-Invexity in Nonlinear Multiobjective Programming.- Multiobjective Fractional Programming.- Multiobjective Nonsmooth Programming.- Composite Multiobjective Nonsmooth Programming.- Continuous-time Programming.

V-INVEX FUNCTIONS AND VECTOR OPTIMIZATION summarizes and synthesizes an aspect of research work that has been done in the area of Generalized Convexity over the past several decades. Specifically, the book focuses on V-invex functions in vector optimization that have grown out of the work of Jeyakumar and Mond in the 1990’s. V-invex functions are areas in which there has been much interest because it allows researchers and practitioners to address and provide better solutions to problems that are nonlinear, multi-objective, fractional, and continuous in nature. Hence, V-invex functions have permitted work on a whole new class of vector optimization applications.
There has been considerable work on vector optimization by some highly distinguished researchers including Kuhn, Tucker, Geoffrion, Mangasarian, Von Neuman, Schaiible, Ziemba, etc. The authors have integrated this related research into their book and demonstrate the wide context from which the area has grown and continues to grow. The result is a well-synthesized, accessible, and usable treatment for students, researchers, and practitioners in the areas of OR, optimization, applied mathematics, engineering, and their work relating to a wide range of problems which include financial institutions, logistics, transportation, traffic management, etc.

The book is a well-synthesized, accessible, and usable treatment of the area for students, researchers and practitioners in OR, optimization, applied mathematics, and engineering, and their work related to a wide range of problems which including financial institutions, logistics, transportation, traffic management, etc. Throughout the optimization community, there is considerable interest in V-invex methods for the strength and the facility they demonstrate with vector optimization problems