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Hands-On Mathematical Optimization with Python
出版済み 3-5週間でお届けいたします。
Title: Hands-On Mathematical Optimization with Python Author: Postek, Krzysztof (Boston Consulting Group, Amsterdam) / Zocca, Alessandro (Vrije Universiteit Amsterdam) / Gromicho, Joaquim A. S. (University of Amsterdam & ORTEC) / Kantor, Jeffrey C. (University of Notre Dame, Indiana) Publisher: Cambridge University Press ISBN: 9781009493505 Cover: PAPERBACK Date: 2025年01月 DESCRIPTION 応用数学、インダストリアルエンジニアリング、オペレーションズリサーチの学部生と大学院生、および関連分野の実務家のための、実践的なPythonベースの数理最適化ガイド。実用的応用に重点を置き、50を超えるJupyterノートブックと広範な演習が用意されているため、理解度をテストできます。 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. * Covers all the mathematical fundamentals needed to understand how to implement and solve optimization problems, with a good balance between applications and theory * Focuses on active learning, with numerous examples, exercises and code samples to build a deeper understanding * Employs more than 50 Jupyter notebooks with optimization applications, allowing students to see how the theoretical constructs drive solutions to real-life problems * Highlights the impact that uncertainty might have on solutions of optimization problems and teaches various approaches to handle it * Explores the choices one needs to make when modeling a real-life problem mathematically 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. 最近チェックした商品
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