JAX implementation of Competitive Gradient Descent algorithms
Project description
JAX-CGD
JAX implementation of Competitive Gradient Descent algorithms for minimax optimization problems.
Features
- ACGD (Adaptive Competitive Gradient Descent): An adaptive variant of competitive gradient descent with momentum
- BCGD (Basic Competitive Gradient Descent): Standard competitive gradient descent algorithm
- Flexible Solvers: Support for different linear algebra solvers (CG, GMRES)
- JAX Integration: Built on JAX for automatic differentiation and JIT compilation
Installation
pip install jax-cgd
Algorithms
ACGD (Adaptive Competitive Gradient Descent)
- Includes momentum and adaptive learning rates
- Better convergence properties for complex minimax problems
- Configurable exponential decay rate (beta) and numerical stability (eps)
BCGD (Basic Competitive Gradient Descent)
- Standard competitive gradient descent
- Simpler implementation for basic minimax optimization
Requirements
- Python >= 3.10
- JAX >= 0.5.1
- NumPy
- Flax >= 1.10.0
License
MIT License
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
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