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PyOED code and documentation are under active development with evolving interface!
PyOED — Optimal Experimental Design PyOED — Optimal Experimental Design

PyOED

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  • Install
  • Quick Start
  • Getting Started
  • Key Concepts
  • Examples & Tutorials
  • FAQ
  • Architecture
  • API Reference
  • Releases
  • News & Changelog
  • Contributors
  • Contributing
  • Feature Requests
  • License
  • PyOED Paper
  • GitLab
  • PyOED Paper

Section Navigation

  • Configs
  • Models
  • Data Assimilation (Inverse Problems)
  • Optimal Experimental Design
  • Optimization
    • Scipy Optimization Interface
    • Policy Optimization
    • Binary Optimization
      • Probabilistic (Stochastic) Binary Optimization
      • Constrained Probabilistic Binary Optimization
      • Greedy Binary Optimization
      • Robust Binary Optimization
      • Constrained Robust Binary Optimization
    • Optimization Test Functions
    • Optimization Utilities
    • Reinforcement-Learning (ML)
  • Statistics
  • Utility
  • Glossary
  • API Reference
  • Optimization
  • Binary Optimization

Binary Optimization#

This subpackage provides access to novel optimization routines for solving binary optimization problems

  • Probabilistic (Stochastic) Binary Optimization
  • Constrained Probabilistic Binary Optimization
  • Greedy Binary Optimization
  • Robust Binary Optimization
  • Constrained Robust Binary Optimization

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Policy Optimization

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Probabilistic (Stochastic) Binary Optimization

Last updated on Feb 26, 2026.

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