Welcome to robust’s documentation!

robust is a framework for engineering system optimization under uncertainty using geometric and signomial programming.

Robust optimization is a tractable stochastic optimization method that protects against uncertain parameters in well-defined sets, and optimizes for the worst case objective.

This website is under construction. If you are not able to find answers to your questions in the documentation. please feel free to post issues and suggest areas for improvement.

Table of contents:

• Robust optimization 101
  • Basic mathematical principles
• Installing robust
  • Dependencies
  • Clone + install robust
• Getting started
• More advanced commands
  • Choosing between RGP approximation methods and uncertainty sets
  • Simulating robust designs
• Why robust optimization?
  • Comparison of general SO methods with RO
• Mathematical moves for robust GPs/SPs
  • Linear programs (LPs) have tractable robust counterparts.
  • Two-term posynomials are LP-approximable.
  • All posynomials are LP-approximable.
  • GPs have robust formulations.
  • RSPs can be represented as sequential RGPs.
• Approximations for tractable robust GPs
  • Simple Conservative Approximation
  • Linearized Perturbations
  • Best Pairs
• Approaches to solving robust SPs
  • General RSP Solver
  • Best Pairs RSP Solver
  • Linearized Perturbations RSP Solver
• Goal programming
  • Implementation
• References

Indices and tables

• Index
• Module Index
• Search Page