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

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.

Gerard Cornuejols is an IBM University Professor of Operations Research at theTepper School of Business, Carnegie Mellon University. Reha Tutuncu is a Vice President in the Quantitative Resources Group at Goldman Sachs Asset Management, New York.

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