Optimization models;
Fundamentals of optimization;
Representation of linear constraints;
Geometry of linear programming;
The simplex method;
Duality and sensitivity;
Enhancements of the simplex method;
Network problems;
Computational complexity of linear programming;
Interior-point methods of linear programming;
Basics of unconstrained optimization;
Methods for unconstrained optimization;
Low-storage methods for unconstrained problems;
Optimality conditions for constrained problems;
Feasible-point methods;
Penalty and barrier methods

"This book introduces the applications, theory, and algorithms of linear and nonlinear optimization, with an emphasis on the practical aspects of the material. Its unique modular structure provides flexibility to accommodate the varying needs of instructors, students, and practitioners with different levels of sophistication in these topics. The succinct style of this second edition is punctuated with numerous real-life examples and exercises, and the authors include accessible explanations of topics that are not often mentioned in textbooks, such as duality in nonlinear optimization, primal-dual methods for nonlinear optimization, filter methods, and applications such as support vector machines