Mathematical modeling, cases -- NLP optimality conditions -- Goodness of optimization algorithms -- Nonlinear Programming algorithms -- Deterministic GO algorithms -- Stochastic GO algorithms.

This self-contained text provides a solid introduction to global and nonlinear optimization, providing students of mathematics and interdisciplinary sciences with a strong foundation in applied optimization techniques. The book offers a unique hands-on and critical approach to applied optimization which includes the presentation of numerous algorithms, examples, and illustrations, designed to improve the reader's intuition and develop the analytical skills needed to identify optimization problems, classify the structure of a model, and determine whether a solution fulfills optimality conditions. Key features of "Introduction to Nonlinear and Global Optimization": - Offers insights into relevant concepts such as "regions of attraction", "branch-and-bound", and "cross-cutting" methods as well as many other useful methodologies. -Exhibits numerical examples and exercises developing the reader's familiarity with the terminology and algorithms that are frequently encountered in scientific literature. - Presents various heuristic and stochastic optimization techniques demonstrating how each be applied to a variety of models from biology, engineering, finance, chemistry, and economics. This book is intended to serve as a primary text in an advanced undergraduate or graduate course focusing on nonlinear and global optimization and requires an understanding of basic calculus and linear algebra.