Hey there! I’m Sam Saberi, a passionate individual with a love for control systems, programming, and optimization. Originally from Iran and now residing in the United States, I hold a Ph.D. in electrical engineering. Throughout my journey, I’ve realized that many tutorials on optimization fall short of providing clear, practical examples. This often leaves students feeling overwhelmed and unable to grasp the concepts of defining and solving optimization problems truly. That’s why I decided to create this Python optimization tutorial series that aims to be informative, user-friendly, and personalized.
Why we are going to use pyomo?
When it comes to optimization modeling with Python, I chose Pyomo as our tool of choice for a variety of reasons. Pyomo offers a fantastic, high-level modeling language that allows us to express optimization problems in a clear and concise manner. It seamlessly integrates with powerful solvers like Ipopt and GLPK, unlocking a world of optimization capabilities. What I find particularly appealing about Pyomo is its strong community support and extensive documentation. This means we’ll have access to plenty of resources, examples, and assistance along the way. Whether you’re a beginner or an experienced practitioner, Pyomo provides a solid foundation for learning and applying optimization techniques.
Together, we’ll embark on this educational journey, exploring various optimization examples and breaking them down into simple, understandable steps. My goal is to help you gain confidence in defining and solving optimization problems using Python, regardless of your background knowledge. So, let’s dive in and unlock Python’s incredible optimization potential together!”
Soon, I’ll be introducing another tutorial series where we’ll tackle a wide range of optimization problems using Julia’s JuMP package, which happens to be my personal favorite among all optimization packages. Stay tuned for an exciting journey into the world of optimization with Julia too, but first we have a lot to do with Pyomo!