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WEAR Hands-On Mathematical Optimization with Python by Krzysztof Postek
US $34.99
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Condition:
“Outside edge of pages and back cover show notable signs of previous shipment incident that caused ”... Read moreabout condition
Good
A book that has been read but is in good condition. Very minimal damage to the cover including scuff marks, but no holes or tears. The dust jacket for hard covers may not be included. Binding has minimal wear. The majority of pages are undamaged with minimal creasing or tearing, minimal pencil underlining of text, no highlighting of text, no writing in margins. No missing pages.
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Located in: Princeton, North Carolina, United States
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eBay item number:205529883986
Item specifics
- Condition
- Good
- Seller Notes
- Book Title
- Hands-On Mathematical Optimization with Python
- ISBN-13
- 9781009493505
- ISBN
- 9781009493505
About this product
Product Identifiers
Publisher
Cambridge University Press
ISBN-10
1009493507
ISBN-13
9781009493505
eBay Product ID (ePID)
19067383888
Product Key Features
Number of Pages
387 Pages
Language
English
Publication Name
Hands-On Mathematical Optimization with Python
Publication Year
2025
Subject
General
Type
Textbook
Subject Area
Mathematics
Format
Trade Paperback
Dimensions
Item Height
0.8 in
Item Length
10 in
Item Width
7 in
Additional Product Features
LCCN
2024-015052
Reviews
'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
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
519.602855133
Table Of Content
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.
Synopsis
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., A hands-on Python-based guide to mathematical optimization for undergraduates and graduates in applied mathematics, industrial engineering and operations research, as well as practitioners in related fields. Focuses on practical applications, with over 50 Jupyter notebooks and extensive exercises to test understanding.
LC Classification Number
QA402.5.P68 2024
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Seller feedback (4,476)
- 5***n (4688)- Feedback left by buyer.Past monthVerified purchaseThanks
- a***s (471)- Feedback left by buyer.Past monthVerified purchaseGreat transaction
- e***e (298)- Feedback left by buyer.Past monthVerified purchasePerfect transaction. Thank you!