Hands-On Mathematical Optimization with Python

Krzysztof Postek is Senior Optimization Data Scientist with the Boston Consulting Group in Amsterdam. He received his Ph.D. in Operations Research in 2017 from Tilburg University. After his postdoc at the Technion – Israel Institute of Technology, he spent several years as a faculty member at Erasmus University Rotterdam and Delft University of Technology. His research interests revolve mostly around optimization under uncertainty. Alessandro Zocca is Assistant Professor in the Department of Mathematics at the Vrije Universiteit Amsterdam. He received his Ph.D. in Mathematics from the University of Eindhoven in 2015. He was a postdoctoral researcher first at CWI Amsterdam, and then at the California Institute of Technology, supported by a NWO Rubicon grant. His work lies in the area of applied probability, learning, and optimization, drawing motivation in particular from applications to power systems reliability. Joaquim A.S. Gromicho acts as Science and Education Officer for ORTEC and is full professor of Business Analytics at the University of Amsterdam. He received his Ph.D. in Optimization in 1995 from the Erasmus University Rotterdam, before spending two years as Assistant Professor at the University of Lisbon. He serves the Dutch Statistics and OR Society as editor in chief of STAtOR, a magazine on applications and impact, and the steering committee of the EURO Practitioner's Forum. Jeffrey C. Kantor earned his Ph.D. in Chemical Engineering from Princeton University in 1981. After a postdoc at the University of Tel Aviv, he joined the Chemical Engineering Department at the University of Notre Dame. His research interests focused on the theory and application of nonlinear control theory and techniques to chemical and biological processes. His awards have included an NSF Presidential Young Investigator Award, a Camille and Henry Dreyfus Research Scholar Award, and is a Fellow of the American Association for the Advancement of Science. He enjoyed modeling for optimization and contributed to the Pyomo community.

'This is a fantastic textbook on optimization! It contains the right mix of theoretical and more practical optimization aspects. Several chapters contain more recent important developments, e.g., conic and robust optimization. Moreover, the Python codes provided make this textbook really 'hands-on'. It is clear that the authors are not only experts in optimization theory, but also have applied optimization in practice themselves.' Dick den Hertog, University of Amsterdam

• 1. Mathematical optimization; 2. Linear optimization; 3. Mixed-integer linear optimization; 4. Network optimization; 5. Convex optimization; 6. Conic optimization; 7. Accounting for uncertainty: Optimization meets reality; 8. Robust optimization; 9. Stochastic optimization; 10. Two-stage problems; Appendix A. Linear algebra primer; Appendix B. Solutions of selected exercises; List of Tables; List of Figures; Index.