TorchOpt: An Efficient Library for Differentiable Optimization

Understand

Recent years have witnessed the booming of various differentiable optimization algorithms.

• These algorithms exhibit different execution patterns, and their execution needs massive computational resources that go beyond a single CPU and GPU.
• Existing differentiable optimization libraries, however, cannot support efficient algorithm development and multi-CPU/GPU execution, making the development of differentiable optimization algorithms often cumbersome and expensive.
• This paper introduces TorchOpt, a PyTorch-based efficient library for differentiable optimization.