A Practical Guide to Optimization in Engineering and Data Science

Name: A Practical Guide to Optimization in Engineering and Data Science
Price: 296.98 AUD
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ISBN: 9783032046321

Wellington Rodrigo Monteiro, Gilberto Reynoso Meza

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Hardback

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English

Springer Nature Switzerland AG
09 January 2026

Technical design; Maths for engineers; Industrial chemistry & manufacturing technologies

This book offers a hands-on and comprehensive guide to optimization techniques tailored for data scientists and engineers, combining theoretical foundations with practical applications. It begins by demystifying core concepts and types of optimization, then explores their relevance across engineering and data science domains. Readers are introduced to essential mathematical tools, single- and multi-objective optimization methods, and a wide range of algorithms including gradient-based techniques, evolutionary strategies, and swarm intelligence. The book also lists real-world applications across industries and provides several Python-based examples, enabling readers to implement and experiment with optimization models in practice. With its structured approach and rich set of examples, this book serves as a valuable resource for professionals and researchers seeking to apply optimization effectively in their work.

By: Wellington Rodrigo Monteiro, Gilberto Reynoso Meza
Imprint: Springer Nature Switzerland AG
Country of Publication: Switzerland
Dimensions: Height: 235mm, Width: 155mm,
ISBN: 9783032046321
ISBN 10: 3032046327
Pages: 325
Publication Date: 09 January 2026
Audience: Professional and scholarly , Undergraduate
Format: Hardback
Publisher's Status: Active

1. Grokking Optimization.- 2. Essential Mathematics for Optimization.- 3. Single-Objective Optimization Techniques.- 4. Metaheuristics for Single-Objective Optimization.- 5. Multi-Objective Optimization.- 6. Applications of Optimization.- 7. Practical Optimization Examples with Python.

Wellington Rodrigo Monteiro received his Ph.D. in Industrial and Systems Engineering from the Pontifical Catholic University of Parana (PUCPR), Brazil, a Master’s in Industrial and Systems Engineering from PUCPR, and a Bachelor’s in Computer Engineering from PUCPR. He has over ten years of experience working as a data scientist in large international corporations and startups. He works as a lead machine learning engineer at Nubank and as an assistant professor at PUCPR. His interests are rooted in machine learning, evolutionary algorithms, and multi-objective optimization applications in the industry. Gilberto Reynoso Meza received his Ph.D. in Automation from the Universitat Politècnica de València (Spain) and his B.Sc. (2001) in Mechanical Engineering from the Tecnológico de Monterrey, Campus Querétaro (Mexico). Currently, he is with the Industrial and Systems Engineering Graduate Program (PPGEPS) of the Pontifical Catholic University of Parana (PUCPR), Brazil, as an associate Professor. His main research interests are computational intelligence methods for control engineering, multi-objective optimization, many-objectives optimization, multi-criteria decision-making, evolutionary algorithms, and machine learning.