ABOUT THIS BOOK
Optimization models play an increasingly important role in financial decisions. This is the first textbook devoted to explaining how recent advances in optimization models, methods and software can be applied to solve problems in computational finance more efficiently and accurately. Chapters discussing the theory and efficient solution methods for all major classes of optimization problems alternate with chapters illustrating their use in modeling problems of mathematical finance. The reader is guided through topics such as volatility estimation, portfolio optimization problems and constructing an index fund, using techniques such as nonlinear optimization models, quadratic programming formulations and integer programming models respectively. The book is based on Master's courses in financial engineering and comes with worked examples, exercises and case studies. It will be welcomed by applied mathematicians, operational researchers and others who work in mathematical and computational finance and who are seeking a text for self-learning or for use with courses.

TABLE OF CONTENTS
1. Introduction
2. Linear programming: theory and algorithms
3. LP models: asset/liability cash flow matching
4. LP models: asset pricing and arbitrage
5. Nonlinear programming: theory and algorithms
6. NLP volatility estimation
7. Quadratic programming: theory and algorithms
8. QP models: portfolio optimization
9. Conic optimization tools
10. Conic optimization models in finance
11. Integer programming: theory and algorithms
12. IP models: constructing an index fund
13. Dynamic programming methods
14. DP models: option pricing
15. DP models: structuring asset backed securities
16. Stochastic programming: theory and algorithms
17. SP models: value-at-risk
18. SP models: asset/liability management
19. Robust optimization: theory and tools
20. Robust optimization models in finance
Appendix A. Convexity
Appendix B. Cones
Appendix C. A probability primer
Appendix D. The revised simplex method
Bibliography
Index.

ABOUT THE AUTHORS
Gerard Cornuejols, Carnegie Mellon University, Pennsylvania
Gerard Cornuejols is an IBM University Professor of Operations Research at theTepper School of Business, Carnegie Mellon University.

Reha Tutuncu, Quantitative Resources Group, Goldman Sachs Asset Management, New York
Reha Tütüncü is a Vice President in the Quantitative Resources Group at Goldman Sachs Asset Management, New York.