Vladislav Bukshtynov

Computational Optimization (eBook, PDF)

Success in Practice

• Format: PDF

Produktbeschreibung

This textbook offers a guided tutorial reviewing the theoretical fundamentals while going through the practical examples used for constructing the computational frame, applied to various real-life models.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 414
• Erscheinungstermin: 17. Februar 2023
• Englisch
• ISBN-13: 9781000834727
• Artikelnr.: 66993850

Autorenporträt

Dr. Vladislav Bukshtynov holds a Ph.D. degree in Computational Engineering & Science from McMaster University. He is an Assistant Professor at the Dept. of Mathematical Sciences of Florida Institute of Technology. He completed a 3-year postdoctoral term at the Dept. of Energy Resources Engineering of Stanford University. He actively teaches and advises students from various fields: applied and computational math, operations research, different engineering majors. His teaching experience includes Multivariable Calculus, Honors ODE/PDE courses for undergrad students; Applied Discrete Math, Linear/Nonlinear Optimization for senior undergrads and graduates. As a researcher, Dr. Bukshtynov leads his research group with several dynamic scientific directions and ongoing collaborations for various cross-institutional and interdisciplinary projects. His current interests lie in but are not limited to the areas of applied and computational mathematics focusing on combining theoretical and numerical methods for various problems in computational/numerical optimization, control theory, and inverse problems.

Inhaltsangabe

Chapter 1. Introduction to Optimization. Chapter 2. Minimization Approaches
for Functions of One Variable. Chapter 3. Generalized Optimization
Framework. Chapter 4. Exploring Optimization Algorithms. Chapter 5. Line
Search Algorithms. Chapter 6. Choosing Optimal Step Size. Chapter 7. Trust
Region and Derivative-Free Methods. Chapter 8. Large-Scale and Constrained
Optimization. Chapter 9. ODE-based Optimization. Chapter 10. Implementing
Regularization Techniques. Chapter 11. Moving to PDE-based Optimization.
Chapter 12. Sharing Multiple Software Environments.