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.
1.1.0
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](https://pypi.org/project/pygranso/).
**Added** - Add examples: ex 10 dictionary learning with torch.nn module; ex 11 orthogonal recurrent neural networks.
1.0.0
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.