A Practical Guide to Optimization in Engineering and Data Science
Description
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.
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Persons
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.
Content
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.