JAXopt
Hardware-accelerated, batchable, and differentiable optimization algorithms implemented in JAX for machine learning research.
What is JAXopt?
JAXopt is a library of optimization algorithms implemented in Google's JAX framework, providing hardware-accelerated, batchable, and differentiable solvers for machine learning and scientific computing. It enables researchers to solve optimization problems efficiently on GPUs and TPUs while maintaining differentiability for gradient-based learning pipelines.
Machine learning researchers and practitioners working with JAX who need efficient, differentiable optimization routines for training models, hyperparameter tuning, or solving implicit equations.
JAXopt offers GPU/TPU-native optimization algorithms with automatic vectorization and differentiation capabilities, making it uniquely suited for modern deep learning workflows where optimization is part of a larger differentiable computation graph.
Overview
Hardware accelerated, batchable and differentiable optimizers in JAX.
Use Cases
Best For
- Training machine learning models with custom optimization algorithms
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