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Constraint Dissolving Approaches for Riemannian Optimization
Welcome to CDOpt
Overview
Installation
Tutorials
Quickstart
Training neural networks with manifold constraints
Define your own manifold
Examples
Optimization via SciPy
Dictionary Learning
Dictionary Learning Accelerated by JIT
Discretized 1D Kohn-Sham Equation
Low-Rank Nearest Correlation Estimation
Bose–Einstein Condensates
Symplectic Eigenvalue Problem
Training Neural Networks with Manifold Constraints via PyTorch
Training LeNet with Constrained Convolution Kernels
Training Single-Layer RNN with Constrained Weights
Training Multi-Layer RNN with Constrained Weights
Training LSTM with Constrained Weights
Time Sequence Prediction with Orthogonality Constrained LSTM
Distributed Training for RNN with Constrained Weights
Distributed Training for A Simple Network by Distributed RPC Framework
Training Neural Networks with Manifold Constraints via JAX and FLAX
Training LeNet with Constrained Convolution Kernels by JAX and FLAX
API Reference
cdopt.core
cdopt.manifold
cdopt.manifold_np
basic_manifold_np
euclidean_np
sphere_np
oblique_np
stiefel_np
generalized_stiefel_np
hyperbolic_np
symp_stiefel_np
cdopt.manifold_torch
basic_manifold_torch
euclidean_torch
sphere_torch
oblique_torch
stiefel_torch
generalized_stiefel_torch
hyperbolic_torch
symp_stiefel_torch
cdopt.nn
cdopt.nn.utils
cdopt.nn.utils.stateless
cdopt.nn.utils.modified_apply
cdopt.nn.utils.set_constraints
cdopt.nn.module
Linear_cdopt
Bilinear
Conv1d_cdopt
Conv2d_cdopt
Conv3d_cdopt
RNNBase_cdopt
RNN_cdopt
LSTM_cdopt
GRU_cdopt
RNNCell_cdopt
LSTMCell_cdopt
GRUCell_cdopt
cdopt.nn.module.utils
cdopt.linen
linen.linear
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