A hands-on Python-based guide to mathematical optimization for undergraduates and graduates, with numerous applications and code samples.
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Da: World of Books (was SecondSale), Montgomery, IL, U.S.A.
Condizione: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc. Codice articolo 00103224174
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Da: BooksRun, Philadelphia, PA, U.S.A.
Paperback. Condizione: New. 1. The item is brand new, never used or read. It's in perfect condition and may include supplements and/or access codes or come shrink-wrapped. Codice articolo 1009493507-10-1
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Da: Kuba Libri, Prague, Repubblica Ceca
Soft cover. Condizione: New. Codice articolo 011875
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Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: New. Codice articolo 47745545-n
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Da: GreatBookPrices, Columbia, MD, U.S.A.
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Da: BargainBookStores, Grand Rapids, MI, U.S.A.
Paperback or Softback. Condizione: New. Hands-On Mathematical Optimization with Python. Book. Codice articolo BBS-9781009493505
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Da: Speedyhen LLC, Hialeah, FL, U.S.A.
Condizione: NEW. Codice articolo NWUS9781009493505
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Da: Rarewaves.com USA, London, LONDO, Regno Unito
Paperback. Condizione: 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. Codice articolo LU-9781009493505
Quantità: Più di 20 disponibili
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
Paperback. Condizione: 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. Codice articolo 9781009493505
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Da: Rarewaves USA, OSWEGO, IL, U.S.A.
Paperback. Condizione: 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. Codice articolo LU-9781009493505
Quantità: Più di 20 disponibili