A library for differentiable nonlinear optimization
Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.
TorchOpt is an efficient library for differentiable optimization built upon PyTorch.
Mathematical Programming in JAX
Safe robot learning
Official code repository for ∇-Prox: Differentiable Proximal Algorithm Modeling for Large-Scale Optimization (SIGGRAPH TOG 2023)
A library for soft differentiable relaxations of common JAX functions.
A library for soft differentiable relaxations of common PyTorch functions.
[L4DC 2025] Automatic hyperparameter tuning for DeePC. Built by Michael Cummins at the Automatic Control Laboratory, ETH Zurich.
Differentiable curve and surface similarity measures.
Decision-Focused Learning (DFL) for day-ahead scheduling of Underground Pumped Hydro Energy Storage (UPHES).
PyTorch differentiable VLSI placer: macro-aware density modeling, two-phase overlap-penalty ramp. Reproducible results: 8.8% wirelength reduction, 99.1% overlap-area reduction vs. unoptimized placement (synthetic 32-cell/535-net benchmark, ~4s runtime, CPU-only). 6/6 tests passing, Docker+pytest CI.
Differentiable PyTorch loss for partial point-cloud alignment and contact-aware 3D optimization.
Differentiable stochastic-computing primitives for PyTorch: train neural networks natively SC-aware.
A fully vectorized PyTorch implementation of ROUGE scores optimized for training neural networks.
Collection of differentiable methods for robotics applications implemented with Pytorch.
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