Description
Book SynopsisThe primary goal of this text is a practical one. Equipping students with enough knowledge and creating an independent research platform, the author strives to prepare students for professional careers. Providing students with a marketable skill set requires topics from many areas of optimization. The initial goal of this text is to develop a marketable skill set for mathematics majors as well as for students of engineering, computer science, economics, statistics, and business. Optimization reaches into many different fields.
This text provides a balance where one is needed. Mathematics optimization books are often too heavy on theory without enough applications; texts aimed at business students are often strong on applications, but weak on math. The book represents an attempt at overcoming this imbalance for all students taking such a course.
The book contains many practical applications but also explains the mathematics behind the techniques, including stating definit
Table of Contents
1. 1. Preamble. 2. The Language of Optimization. 3. Computational Complexity. 4. Algebra Review. 5. Matrix Factorization. 6. Linear Programming. 7. Sensitivity Analysis. 8. Integer Linear Programing. 9. Calculus Review. 10. A Calculus Approach to Nonlinear Programming. 11. Constrained Nonlinear Programming: Lagrange Multipliers and the KKT Conditions. 12. Optimization involving Quadratic Forms. 13. Iterative Methods. 14. Derivative-Free Methods. 15. Search Algorithms. 16. Important Sets for Optimization. 17. The Fundamental Theorem of Linear Programming. 18. Convex Functions. 19. Convex Optimization. 20. An Introduction to Combinatorics. 21. An Introduction to Graph Theory. 22. Network Flows. 23. Minimum-Weight Spanning Trees and Shortest Paths. 24. Network Modeling and the Transshipment Problem. 25. The Traveling Salesperson Problem. Probability. 27. Regression Analysis via Least Squares. 28. Forecasting. 29. Introduction to Machine Learning.
