Hands-On Mathematical Optimization with Python
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20 238 Ft
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Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
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Product details:
- Publisher Cambridge University Press
- Date of Publication 16 January 2025
- ISBN 9781009493505
- Binding Paperback
- No. of pages354 pages
- Size 254x178x20 mm
- Weight 680 g
- Language English 679
Categories
Short description:
A hands-on Python-based guide to mathematical optimization for undergraduates and graduates, with numerous applications and code samples.
MoreLong description:
This practical guide to optimization combines mathematical theory with hands-on coding examples to explore how Python can be used to model problems and obtain the best possible solutions. Presenting a balance of theory and practical applications, it is the ideal resource for upper-undergraduate and graduate students in applied mathematics, data science, business, industrial engineering and operations research, as well as practitioners in related fields. Beginning with an introduction to the concept of optimization, this text presents the key ingredients of an optimization problem and the choices one needs to make when modeling a real-life problem mathematically. Topics covered range from linear and network optimization to convex optimization and optimizations under uncertainty. The book's Python code snippets, alongside more than 50 Jupyter notebooks on the author's GitHub, allow students to put the theory into practice and solve problems inspired by real-life challenges, while numerous exercises sharpen students' understanding of the methods discussed.
'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
Table of Contents:
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.
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