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Written by an experienced operations research practitioner with a strong applied mathematics background, this book offers practical insights into how to approach optimization problems, how to develop intelligent and efficient mathematical models and algorithms, and how to implement and deliver software products to customers. With a focus on revealing the similarities and differences between academia and industry in mathematical modeling, the book provides useful tips and advice based on the author s extensive experience as a principal developer working to solve real-world optimization problems for several major high-tech companies.
The book offers valuable food for thought for researchers and practical guidance for graduate students preparing for their future projects in the industry. It is also an essential resource for practitioners working in the industrial, business, and service sectors.
The book s presentation style is very enjoyable, and Zak keeps the focus on the practical aspects of the problems as they occur in the industry. Though practitioners are the primary target audience, students and academics can also benefit from the book as are reference to the current state of the art. (Paparao Kavalipati, Computing Reviews, January 3, 2025)
Sommario
Practical tips.- Real-world problems.
Info autore
Eugene Zak is an applied mathematician and enterprise-level software developer. He holds a Ph.D. in Applied Mathematics from Moscow Institute of Physics and Technology, Russia, and received the INFORMS 2011 Edelman Prize in Operations Research and Analytics. His areas of expertise include mathematical modeling, optimization, and software design and development. Eugene has worked at Amazon, Alstom Grid, IBM, and a number of start-up companies.
Relazione
The book s presentation style is very enjoyable, and Zak keeps the focus on the practical aspects of the problems as they occur in the industry. Though practitioners are the primary target audience, students and academics can also benefit from the book as are reference to the current state of the art. (Paparao Kavalipati, Computing Reviews, January 3, 2025)
