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WEAR Hands-On Mathematical Optimization with Python by Krzysztof Postek

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A book that has been read, but is in good condition. Minimal damage to the book cover eg. scuff marks, but no holes or tears. If this is a hard cover, the dust jacket may be missing. Binding has minimal wear. The majority of pages are undamaged with some creasing or tearing, and pencil underlining of text, but this is minimal. No highlighting of text, no writing in the margins, and no missing pages. See the seller’s listing for full details and description of any imperfections. See all condition definitionsopens in a new window or tab
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“Outside edge of pages and back cover show notable signs of previous shipment incident that caused ...
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
Publication Name
Hands-On Mathematical Optimization with Python
Language
English
Subject
General
Publication Year
2025
Type
Textbook
Subject Area
Mathematics
Author
Alessandro Zocca, Joaquim A. S. Gromicho, Krzysztof Postek, Jeffrey C. Kantor
Format
Trade Paperback

Dimensions

Item Height
0.8 in
Item Length
10 in
Item Width
7 in

Additional Product Features

LCCN
2024-015052
Dewey Edition
23
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
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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