Constrained optimization toolkit for PyTorch

Efficient Householder Transformation in PyTorch

Spectral Tensor Train Parameterization of Deep Learning Layers

[IEEE Access 2022] Revisiting Orthogonality Regularization: A Study for Convolutional Neural Networks in Image Classification

In SIAM's Int. Autodiff Conf Oral ['24] Algos for differentiable LQ matrix decomposition for all matrix orders.

Plotting the loss of Orthogonality of a matrix at each iteration step due to four different methods of Orthogonalization

第四届“华为杯”无线通信算法大赛:LoMACS-SVDNet: PyTorch model for MIMO SVD (no QR/SVD/EVD), orthogonality via NOR, FFT gating, projected attention, structured pruning. Score 63 — 4th (Third Prize).

Co-clustering algorithms can seek homogeneous sub-matrices into a dyadic data matrix, such as a document-word matrix.

Vectors, matrices, linear equations, Gaussian elimination, vector geometry with dot product and vector product, determinants, vector spaces, linear independence, bases, change of basis, linear transformations, the least-squares method, eigenvalues, eigenvectors, quadratic forms, orthogonality, inner-product space, Gram-Schmidt's method.

Model reduction of 2D diffusion equation

Applied Linear Algebra - 7th Semester

A small C-library for Linear Algebra functions that do complex matrix calculations.

Basic and advanced linear algebra and numerical problems, numerical algorithms, and techniques with multiple applications in the field of Computer Science.

A set of codes in MATLAB for ODE reconstruction using least-square method and orthogonal polynomials

🤖 Enhance wireless communication with an AI-enabled SVD operator that predicts channel parameters without complex decompositions, ensuring efficiency and accuracy.