Computational Optimization in Python (São Paulo, Brazil)

Welcome! This website is the main page for our shortcourse, PRO5996 - Otimização Computacional em Python.

Learning Objectives

1. Explore optimization modeling paradigms including (non)linear programming, mixed integer programming, disjunctive programming, dynamic optimization, and stochastic programming

2. Implement and solver computational optimization problems in the Pyomo ecosystem

3. Explore data science methods including nonlinear regression and model-based design of experiments

Participation

All attendees should bring a laptop computer with an internet connection. All examples run in Google Colab, which requires a Google account. (For example, a personal Gmail account works fine.) Participants should also bring pencil and paper or an tablet to take notes. All instruction will be in English.

Schedule

Monday, October 21, 2024

• Optimization Modeling with Applications

• Your First Optimization Problem

• Continuous Optimization: Linear Programming

• Continuous Optimization: Nonlinear Programming

• 60 Minutes to Pyomo: An Energy Storage Model Predictive Control Example

Tuesday, October 22, 2024

• Integer Programs

• Logical Modeling and Generalized Disjunctive Programs

• Modeling Disjunctions through the Strip Packing Problem

• Stochastic Programming

Wednesday, October 23, 2024

• Blocks and Other Pyomo Best Practices

• Pyomo.DAE Example: Race Car

• Pyomo.DAE Example: Temperature Control Lab

Thursday, October 24, 2024

• Nonlinear Parameter Estimation

• Model-based Design of Experiments

• Optimizing Experiments with Pyomo.DoE

Practice Problems

• Pyomo Homework 1

• Pyomo Homework 2

• Pyomo Homework 3

• Pyomo Mini-Project: Receding Horizon Stochastic Control