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  • ND Pyomo Cookbook
  • 1. Getting Started with Pyomo
    • 1.1. Installing a Pyomo/Python Development Environment
    • 1.2. Running Pyomo on Google Colab
    • 1.3. Running Pyomo on the Notre Dame CRC Cluster
    • 1.4. Cross-Platform Installation of Pyomo and Solvers
  • 2. Linear Programming
    • 2.1. Production Models with Linear Constraints
    • 2.2. Production Model Sensitivity Analysis
    • 2.3. Linear Blending Problem
    • 2.4. Design of a Cold Weather Fuel for a Camping Stove
    • 2.5. Gasoline Blending
    • 2.6. Model Predictive Control of a Double Integrator
    • 2.7. Non-Continuous Objectives
  • 3. Assignment Problems
    • 3.1. Transportation Networks
  • 4. Scheduling with Disjunctive Constraints
    • 4.1. Introduction to Disjunctive Programming
    • 4.2. Machine Bottleneck
    • 4.3. Job Shop Scheduling
    • 4.4. Maintenance Planning
    • 4.5. Scheduling Multipurpose Batch Processes using State-Task Networks
    • 4.6. Unit Commitment
  • 5. Simulation
    • 5.1. Response of a First Order System to Step and Square Wave Inputs
    • 5.2. Exothermic CSTR
    • 5.3. Transient Heat Conduction in Various Geometries
    • 5.4. Diffusion with Adsorption in Polymers
  • 6. Differential-Algebraic Equations
    • 6.1. Unconstrained Scalar Optimization
    • 6.2. Maximizing Concentration of an Intermediate in a Batch Reactor
    • 6.3. Path Planning for a Simple Car
    • 6.4. Soft Landing Apollo 11 on the Moon
    • 6.5. Ascent from Lunar Surface
  • 7. Parameter Estimation
    • 7.1. Parameter estimation
  • 8. Financial Applications
    • 8.1. Obtaining Historical Stock Data
    • 8.2. Consolidating and Charting Stock Data
    • 8.3. Binomial Model for Pricing Options
    • 8.5. MAD Portfolio Optimization
    • 8.6. Real Options
  • 9. Guide to Coding with Pyomo
    • 9.1. Notebook Style Guide
  • 10. Index
  • Repository
  • Open issue

Index

Index

I | S

I

  • ipopt

S

  • Simulator, [1]

By Jeffrey C. Kantor

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