admm

Contents

  • 1. Overview
  • 2. Installation
    • 2.1. Supported Platforms
    • 2.2. Install ADMM Python Library
      • 2.2.1. Install from PyPI
      • 2.2.2. Install from Source
      • 2.2.3. Isolated Python Environment
      • 2.2.4. Installation Verification
      • 2.2.5. Troubleshooting
  • 3. User Guide
    • 3.1. Minimal Model
    • 3.2. Supported Problem Structure
      • 3.2.1. Map This Abstract Form to Code
      • 3.2.2. Support Boundary
    • 3.3. Modeling Workflow
    • 3.4. Variables
      • 3.4.1. Choose the Shape First
      • 3.4.2. Attributes vs. Explicit Constraints
      • 3.4.3. Accessing Solution Values
      • 3.4.4. Warm Start
      • 3.4.5. Basic Operators
      • 3.4.6. Variable Shapes and Transformations
    • 3.5. Parameters
    • 3.6. Objective
      • 3.6.1. Build the Objective in Layers
      • 3.6.2. Common Objective Ingredients
    • 3.7. Constraints
      • 3.7.1. How to Choose the Direct Form
      • 3.7.2. Walkthrough: Projecting Onto a Feasible Set
    • 3.8. Solver Options
    • 3.9. Solve the Model
      • 3.9.1. Read Results in This Order
      • 3.9.2. Error Handling and Troubleshooting
      • 3.9.3. What to Do When the Solve Does Not Succeed
    • 3.10. Model Save and Reload
      • 3.10.1. What Is Saved
      • 3.10.2. What Is Not Saved
      • 3.10.3. Compression
      • 3.10.4. Example: Save, Reload, and Re-solve
    • 3.11. Supported Building Blocks
      • 3.11.1. Three Modeling Roles
      • 3.11.2. Worked Example: One Model, Three Roles
      • 3.11.3. Elementwise Atoms
      • 3.11.4. Vector and Matrix Atoms
      • 3.11.5. Structural Variable Attributes and Domain Constraints
      • 3.11.6. Compact Atom Reference
    • 3.12. Symbolic Canonicalization
      • 3.12.1. Write Clean Math First
      • 3.12.2. Walkthrough: Readable Math, Recognized Structure
      • 3.12.3. Mathematical Rewrite Examples
      • 3.12.4. Transform Examples With Auxiliary Variables
    • 3.13. User-Defined Proximal Extensions
      • 3.13.1. What a UDF Provides
      • 3.13.2. Walkthrough: The L0 Norm as a UDF
  • 4. Examples
    • 4.1. Linear Program
    • 4.2. Quadratic Program
    • 4.3. Semidefinite Program
    • 4.4. Second-Order Cone Program
    • 4.5. Least Squares
    • 4.6. Ridge Regression
    • 4.7. Huber Regression
    • 4.8. Sparse Logistic Regression
    • 4.9. SVM with L1 Regularization
    • 4.10. Quantile Regression
    • 4.11. Robust PCA
    • 4.12. Sparse Inverse Covariance Selection
    • 4.13. Entropy Maximization
    • 4.14. Fault Detection
    • 4.15. Water Filling
    • 4.16. Convolutional Image Deblurring
    • 4.17. Portfolio Optimization
    • 4.18. L0 Norm
    • 4.19. L0 Ball Indicator
    • 4.20. L1/2 Quasi-Norm
    • 4.21. Group Sparsity
    • 4.22. Matrix Rank Function
    • 4.23. Rank-r Indicator
    • 4.24. The Unit-Sphere Indicator
    • 4.25. The Stiefel-Manifold Indicator
    • 4.26. The Simplex Indicator
    • 4.27. The Binary Indicator
    • 4.28. L0-Regularized Regression
  • 5. Python API
    • 5.1. Global functions
      • 5.1.1. Functions
    • 5.2. ADMMError
      • ADMMError
    • 5.3. OptionConstClass
      • OptionConstClass
    • 5.4. TensorLike
      • TensorLike
    • 5.5. Constant
      • Constant
    • 5.6. Var
      • Var
    • 5.7. Param
      • Param
    • 5.8. Constr
      • Constr
    • 5.9. TuningContext
      • TuningContext
    • 5.10. UDFBase
      • UDFBase
    • 5.11. Model
      • Model
    • 5.12. ArrayLike
      • ArrayLike
  • 6. Citing ADMM
  • 7. Contact
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© Copyright 2026, Decision Intelligence Lab @ DAMO Academy. Last updated on Apr 11, 2026.

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