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Change Log

PyGRANSO: A PyTorch-enabled port of GRANSO with auto-differentiation

Version: 1.2.0 --- 2022-07-26

Description: major fixes and improvements on LBFGS.

Fixed

  • Reducing memory usage for LBFGS. Now PyGRANSO can solve problem with ~15k parameters by using 14 GB memory.
  • Update example: ortho RNN with max folding and orthonormal initialization.
  • Allow high precision for QP solver.
  • Allow part of optimization variables not showing up in objective (see SVM example).
  • Fixed Code 12: terminated with steering failure.
  • Fixed stationary failure: try different stationarity calculation, or set stationarity measure to be inf if encounter numerical issue

Added

  • Reorganize and add examples: perceptual/lp norm attack on ImageNet images. trace optimization with orthogonal constraints; unconstrained deep learning with LeNet5; logistic regression.

Version: 1.1.0 --- 2022-02-20

Description: major fixes and improvements.

Fixed

  • Avoid gradient accumulating in deep learning problem;
  • Prevent memory leak problem when using torch tensor. See ex6 perceptual attack.

Changed

  • Update format of user-defined variables when using pygranso interface.

Packaging

  • Publish pygranso package on Pypi.

Added

  • Add examples: ex 10 dictionary learning with torch.nn module; ex 11 orthogonal recurrent neural networks.

Version: 1.0.0 --- 2021-12-27

Description: initial public release of PyGRANSO.

Main features: auto-differentiation, GPU acceleration, tensor input, scalable QP solver, and zero dependency on proprietary packages. Multiple new examples added.