Tutorial on MATLAB and Python for Optimization
The two packages used throughout this course are the APMonitor Optimization Suite (APM MATLAB or APM Python) and the more recent Python GEKKO package (see GEKKO Documentation).
Python GEKKOAPM MATLAB
APM MATLAB
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Course Information
- Syllabus
- Book Chapters
- Info Sheet
- Schedule
- Expectations
- Competencies
- Optimization Software
- YouTube Playlist
Homework
- Optimization Basics
- Tubular Column
- Two Bar Truss
- Step Cone Pulley
- Beam Column
- Crane Hook
- Rocket Launch
- Spring Design
- Heat Integration
- Slurry Pipeline
- Oxygen Storage
- Quasi-Newton Methods
- Discrete Design
- Simulated Annealing
- KKT Conditions
- Interior Point Method
Projects
Activities
- 1-MATLAB and Python
- 2-Equation Residuals
- 3-Financial Objectives
- 4-Parallel Computing
- 5-Advanced Programming
- 6-Logical Conditions
- 7-Simulated Annealing
- 8-Climate Control
- 9-Dynamic Estimation
- 10-Vapor Liquid Equilibrium
- 11-Ethyl Acetate Kinetics
- 12-Dye Fading Kinetics
- 13-Linear Regression
- 14-Nonlinear Regression
- 15-Knapsack Optimization
- 16-Schedule Optimization
Lecture Notes
- Optimization Introduction
- Mathematical Modeling
- Unconstrained Optimization
- Discrete Optimization
- Genetic Algorithms
- Constrained Optimization
- Robust Optimization
- Dynamic Optimization
Extra Content
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