smt-optim

Introduction

smt-optim is an open-source python package for Bayesian Optimization developed for research purposes. It is well suited for solving expensive-to-evaluate blackbox problem with little exploitable properties such as derivatives. It can handle constrained and multi-fidelity global optimization problems.

Focus on multi-fidelity

smt-optim is designed for multi-fidelity optimization with hierarchical levels of fidelity to reduce the optimization cost. The MFSEGO acquisition strategy judiciously select low fidelity and high fidelity levels when sampling the blackbox functions.

Focus on modular framework

smt-optim is designed from the get-go to be modular, allowing users to swap components such as surrogate models, acquisition strategies and acquisition functions while maintaining an overall standard structure, ideal for research benchmarking.

Contents:

• Get started
  • Installation
  • Usage
• Examples
• Concepts
  • Bayesian Optimization
  • Efficient Global Optimization (EGO)
• Examples
  • Unconstrained optimization - EGO
  • Constrained optimization - SEGO
  • Mutli-fidelity optimization - MFSEGO
  • Benchmark problems
  • Logging
• Miscellaneous
  • Architecture
  • Performance
• API reference
  • Subpackages
  • Submodules
  • smt_optim.acquisition_functions module
  • smt_optim.frameworks module