CBE60499¶

Index of Python Libraries used in this Repository¶

helper¶

  • 1.0.2 Cloud Computing with Google Colab
  • 1.1 60 Minutes to Pyomo: An Energy Storage Model Predictive Control Example
  • 1.2.1 Assignment Goals
  • 2.1 Continuous Optimization
  • 2.2 Integer Programs
  • 2.3 Logical Modeling and Generalized Disjunctive Programs
  • 2.4 Dynamic Optimization: Differential Algebraic Equations (DAEs))
  • 2.6 Dynamic Optimization with Pyomo.DAE
  • 2.8 Parameter estimation with parmest
  • 2.10 Pyomo Homework 1
  • 2.11 Pyomo Homework 2
  • 4.1 Convexity Revisited
  • 4.5 Second Order Optimality Conditions
  • 4.6 NLP Diagnostics with Degeneracy Hunter

matplotlib¶

  • 1.0.3.5 Visualize the Solution
  • 1.1 60 Minutes to Pyomo: An Energy Storage Model Predictive Control Example
  • 1.2.1 Assignment Goals
  • 2.1.2.2 Implement in Pyomo
  • 2.1.2.2 Implement in Pyomo
  • 2.4.3 DAE Formulations for Simple Pendulum Example
  • 2.5 Numeric Integration for DAEs
  • 2.5.2.4 Code
  • 2.6.1.1 Orthogonal Collocation on Finite Elements: Manual Approach
  • 2.8 Parameter estimation with parmest
  • 2.9 Supplementary material: data for parmest tutorial
  • 3.1 Linear Algebra Review and SciPy Basics
  • 3.2 Mathematics Primer
  • 3.3 Unconstrained Optimality Conditions
  • 3.4.1 Test Problem: Example 2.19
  • 3.5 Quasi-Newton Methods for Unconstrained Optimization
  • 3.6 Descent and Globalization
  • 3.7 Algorithms Homework 1
  • 3.8 Algorithms Homework 2
  • 3.8 Algorithms Homework 2
  • 3.9.2.1 Library of Helper Functions
  • 4.1.3.1 Optimization Model and Pyomo Implementation
  • 4.1.3.1 Optimization Model and Pyomo Implementation
  • 4.2.3 Kinematic Interpretation via Example
  • 4.2.3 Kinematic Interpretation via Example
  • 4.4.3 Example
  • 4.4.3 Example
  • 4.7.1 Helper Functions
  • 4.8.1 Helper Functions
  • 4.9.3.2 Problem 2: Convex
  • 4.9.3.3 Problem 3: Nonconvex

numpy¶

  • 1.0.3.5 Visualize the Solution
  • 1.1 60 Minutes to Pyomo: An Energy Storage Model Predictive Control Example
  • 1.2.1 Assignment Goals
  • 2.1.2.2 Implement in Pyomo
  • 2.4.3 DAE Formulations for Simple Pendulum Example
  • 2.5 Numeric Integration for DAEs
  • 2.5.2.4 Code
  • 2.8 Parameter estimation with parmest
  • 2.9 Supplementary material: data for parmest tutorial
  • 3.1 Linear Algebra Review and SciPy Basics
  • 3.2 Mathematics Primer
  • 3.3 Unconstrained Optimality Conditions
  • 3.4.1 Test Problem: Example 2.19
  • 3.5 Quasi-Newton Methods for Unconstrained Optimization
  • 3.6 Descent and Globalization
  • 3.7 Algorithms Homework 1
  • 3.8 Algorithms Homework 2
  • 3.9.2.1 Library of Helper Functions
  • 4.1.3.1 Optimization Model and Pyomo Implementation
  • 4.2.3 Kinematic Interpretation via Example
  • 4.4.3 Example
  • 4.5.5.1 Calculation with numpy
  • 4.7.1 Helper Functions
  • 4.8.1 Helper Functions
  • 4.9.2.2 Python Implementation

pandas¶

  • 1.1 60 Minutes to Pyomo: An Energy Storage Model Predictive Control Example
  • 1.2.1 Assignment Goals
  • 2.1 Continuous Optimization
  • 2.8 Parameter estimation with parmest
  • 2.9 Supplementary material: data for parmest tutorial
  • 2.10 Pyomo Homework 1
  • 2.11 Pyomo Homework 2
  • 4.1 Convexity Revisited

pyomo¶

  • 1.0.3.2 Define the Model in Pyomo
  • 1.1 60 Minutes to Pyomo: An Energy Storage Model Predictive Control Example
  • 1.2.1 Assignment Goals
  • 2.1 Continuous Optimization
  • 2.2.1.2 Solve with Continuous Cost Model in Pyomo
  • 2.2.2.1 Linear Program (Relaxation))
  • 2.8.5.2 Parameter estimation with parmest
  • 2.8.6.1 Parameter estimation with parmest
  • 4.1 Convexity Revisited
  • 4.3.3 Multipliers in Pyomo
  • 4.5.5.2.1 Define and solve the model
  • 4.6.1 Setup

random¶

  • 2.1.2.2 Implement in Pyomo
  • 4.1.3.1 Optimization Model and Pyomo Implementation

scipy¶

  • 2.5 Numeric Integration for DAEs

sympy¶

  • 3.8 Algorithms Homework 2

sys¶

  • 1.0.2 Cloud Computing with Google Colab
  • 1.1 60 Minutes to Pyomo: An Energy Storage Model Predictive Control Example
  • 1.2.1 Assignment Goals
  • 2.1 Continuous Optimization
  • 2.2 Integer Programs
  • 2.3 Logical Modeling and Generalized Disjunctive Programs
  • 2.4 Dynamic Optimization: Differential Algebraic Equations (DAEs))
  • 2.6 Dynamic Optimization with Pyomo.DAE
  • 2.8 Parameter estimation with parmest
  • 2.10 Pyomo Homework 1
  • 2.11 Pyomo Homework 2
  • 4.1 Convexity Revisited
  • 4.5 Second Order Optimality Conditions
  • 4.6 NLP Diagnostics with Degeneracy Hunter

time¶

  • 1.2.3.4 Impact of Horizon Length