Inhaltsangabe
A hands-on Python-based guide to mathematical optimization for undergraduates and graduates, with numerous applications and code samples.
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Über die Autorin bzw. den Autor
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.
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Paperback. Zustand: New. 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. Bestandsnummer des Verkäufers LU-9781009493505
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Hands-On Mathematical Optimization with Python (Paperback)
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Paperback. Zustand: new. Paperback. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9781009493505
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Hands-On Mathematical Optimization with Python
Verlag:
Cambridge University Press, GB, 2025
ISBN 10: 1009493507
ISBN 13: 9781009493505
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Paperback. Zustand: New. 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. Bestandsnummer des Verkäufers LU-9781009493505
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