TorchOpt is an efficient library for differentiable optimization built upon PyTorch.
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Updated
May 24, 2024 - Python
TorchOpt is an efficient library for differentiable optimization built upon PyTorch.
Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.
Preliminary code for the paper "Learning Deterministic Surrogates for Robust Convex QCQPs".
Official code repository for ∇-Prox: Differentiable Proximal Algorithm Modeling for Large-Scale Optimization (SIGGRAPH TOG 2023)
Safe robot learning
Automatic hyperparameter tuning for DeePC. Built by Michael Cummins at the Automotaic Control Laboratory, ETH Zurich.
A library for differentiable nonlinear optimization
Collection of differentiable methods for robotics applications implemented with Pytorch.
Tutorial on Deep Declarative Networks
Official repo for the paper "SAGE: SLAM with Appearance and Geometry Prior for Endoscopy" (ICRA 2022)
Implementation and examples from Trajectory Optimization with Optimization-Based Dynamics https://arxiv.org/abs/2109.04928
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