Parallel Computing in Optimization
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Course Information
- Overview
- Syllabus
- Schedule
- Book Chapters 📒
- Info Sheet
- Expectations
- Competencies
- Optimization Software
- YouTube Playlist
Homework
- Optimization Basics
- Optimize with Python
- Tubular Column
- Two Bar Truss
- Step Cone Pulley
- Beam Column
- Crane Hook
- Rocket Launch
- Spring Design
- Heat Integration
- Slurry Pipeline
- Oxygen Furance
- 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
- 17-Global Optimization
- 18-Nonlinear Pricing
Lecture Notes
- Optimization Introduction
- Mathematical Modeling
- Unconstrained Optimization
- Discrete Optimization
- Genetic Algorithms
- Constrained Optimization
- Robust Optimization
- Dynamic Optimization
Extra Content
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