Table of content:
Preface; Part I. Basics: 1. Optimization models; 2. Fundamentals of optimization; 3. Representation of linear constraints; Part II. Linear Programming: 4. Geometry of linear programming; 5. The simplex method; 6. Duality and sensitivity; 7. Enhancements of the simplex method; 8. Network problems; 9. Computational complexity of linear programming; 10. Interior-point methods of linear programming; Part III. Unconstrained Optimization: 11. Basics of unconstrained optimization; 12. Methods for unconstrained optimization; 13. Low-storage methods for unconstrained problems; Part IV. Nonlinear Optimization: 14. Optimality conditions for constrained problems; 15. Feasible-point methods; 16. Penalty and barrier methods; Part V. Appendices: Appendix A. Topics from linear algebra; Appendix B. Other fundamentals; Appendix C. Software; Bibliography; Index.