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Nonlinear and Stochastic Optimization
Nonlinear and Stochastic Optimization
Organization
Welcome
Syllabus
Spring 2023 Calendar
Semester Project
Contribution Instructions
Assignments
Pyomo Homework 1
Pyomo Homework 2
Pyomo Homework 3
Pyomo Mini-Project: Receding Horizon Stochastic Control
Algorithms Homework 1
Algorithms Homework 2
Algorithms Homework 3
Algorithms Homework 4: Interior Point Methods
Optimization Modeling in Pyomo
1. Getting Started with Pyomo
1.1. Local Installation
1.2. Optimization Modeling with Applications
1.3. Your First Optimization Problem
1.4. Continuous Optimization
1.5. Integer Programs
1.6. 60 Minutes to Pyomo: An Energy Storage Model Predictive Control Example
2. Logical Modeling
2.1. Logical Modeling and Generalized Disjunctive Programs
2.2. Modeling Disjunctions through the Strip Packing Problem
3. Dynamic Optimization
3.1. Differential Algebraic Equations (DAEs)
3.2. Numeric Integration for DAEs
3.3. Dynamic Optimization with Collocation and Pyomo.DAE
3.4. Dynamic Optimization with Pyomo.DAE: An Example
4. Optimization Under Uncertainty
4.1. Stochastic Programming
4.2. Risk Measures and Portfolio Optimization
5. Data Science and Applied Statistics
5.1. Parameter estimation with
parmest
5.2. Supplementary material: data for parmest tutorial
5.3. Reactor Kinetics Example for Pyomo.DoE Tutorial
Algorithms and Theory
6. Unconstrained Nonlinear Optimization
6.1. Linear Algebra Review and SciPy Basics
6.2. Mathematics Primer
6.3. Unconstrained Optimality Conditions
6.4. Newton-type Methods for Unconstrained Optimization
6.5. Quasi-Newton Methods for Unconstrained Optimization
6.6. Descent and Globalization
7. Constrained Nonlinear Optimization
7.1. Convexity Revisited
7.2. Local Optimality Conditions
7.3. Analysis of KKT Conditions
7.4. Constraint Qualifications
7.5. Second Order Optimality Conditions
7.6. NLP Diagnostics with Degeneracy Hunter
7.7. Simple Netwon Method for Equality Constrained NLPs
7.8. Inertia-Corrected Netwon Method for Equality Constrained NLPs
8. Special Topics
8.1. Integer Programming with Simple Branch and Bound
8.2. MINLP Algorithms
8.3. Deterministic Global Optimization
Student Contributions
Derivative-Free Optimization
Bayesian Optimization Tutorial 1
Bayesian Optimization Tutorial 2
Stochastic Gradient Descent
Stochastic Gradient Descent Tutorial 1
Stochastic Gradient Descent Tutorial 2
Stochastic Gradient Descent Tutorial 3
Machine Learning and Applied Statistics
Expectation Maximization Algorithm and MAP Estimation
Binder
Colab
repository
open issue
.ipynb
.pdf
MINLP Algorithms
8.2.
MINLP Algorithms
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