Basics: 1. Optimization models; 2. Fundamentals of optimization; 3. Representation of linear constraints; Part II. Linear Programming: 4. Geometry of linear programming; 5.
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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; Interior-point methods of linear programming; Part III.
Unconstrained Optimization: Basics of unconstrained optimization; Methods for unconstrained optimization; Low-storage methods for unconstrained problems; Part IV. Nonlinear Optimization: Optimality conditions for constrained problems; Feasible-point methods; Penalty and barrier methods; Part V.
Appendices: Appendix A. Topics from linear algebra; Appendix B. Other fundamentals; Appendix C. Software; Bibliography; Index. His research focuses on theory and methods of nonlinear optimization and their application to problems in science and engineering. Stephen Nash received a B. Honors degree in mathematics in from the University of Alberta, Canada; and a Ph.
His research activities are centered in scientific computing, especially nonlinear optimization, along with related interests in statistical computing and optimal control. Ariela Sofer received the B.
She received the D. Her major areas of interest are nonlinear optimization, and optimization in biomedical applications. She has been a member of the editorial boards of the journals Operations Research and Management Science, and is coeditor on a subseries of the Annals of Operations Research on Operations Research in Medicine.
Linear and nonlinear optimization
Linear and Nonlinear Optimization