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
Biography
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.
