Most of the books in optimization are devoted to details of one or two aspects of the subject e.g. linear and nonlinear programming, stochastic programming, optimal control, stochastic dynamic programming, mixed integer programming, heuristic methods, or multi-objective programming etc., or are written for a specific discipline. The wide scope of optimization mandates extensive interaction between various disciplines in the development of the methods and algorithms, and in their fruitful application to real world problems. This book presents a discipline independent view of optimization for scientists, researchers, and analysts in various fields. It provides them opportunities to identify and apply algorithms, methods and tools from the diverse areas of optimization to their own field without getting into too much detail about the underlying theories. This work is for researchers in various fields as well as undergraduate and graduate students in engineering, management science, and decision science.