Summary
Explore a comprehensive summary of mastering optimization with Python by reviewing the core concepts and practical methods covered in the course. Understand how to define problems, apply derivatives, use exact and approximate algorithms, work with population heuristics, address constraints, and solve linear programming challenges effectively.
It’s been a long way, but we’ve finished it! In this lesson, we’re going to summarize what we’ve learned during the course.
Introduction
In the first chapter of the course, we learned what optimization is and how it influences our lives. We set the prerequisites to get the best result out of the course and learned about all the tools we’d use. Then we learned the general definition of an optimization problem, how to write an optimization problem in mathematical form, and solved some simple optimization problems using plots.
Derivatives and gradients
Here we learned about derivatives in one and multiple dimensions, and how they can be used to solve optimization problems. We defined and calculated derivatives, gradients, and Hessians of functions. We learned our first exact optimization algorithms and used SymPy to ...