Pytorch implementation of the hamburger module from the ICLR 2021 paper "Is Attention Better Than Matrix Decomposition"

A sparsity aware implementation of "Enhanced Network Embedding with Text Information" (ICPR 2018).

Converts unitary matrix to Qiskit/Cirq/Q# cirquit

Linear Algebra and Statistics library for Scala.js, JVM, and Native.

The official implementation of "Rank-One Network: An Effective Framework for Image Restoration" via TensorFlow

Robust Orthonormal Subspace Learning in Python

This package contains a data structure that wraps a matrix of matrices or factorizations and acts like the matrix resulting from concatenating the input matrices without allocating further memory.

This repository contains Python functions implementing the Hoffman algorithm for decomposing elements of SO(N), and functions applying this algorithm to decompose any element of the matchgate group into nearest-neighbor matchgates. See README for more information and a list of references on the Hoffman decomposition and the matchgate group.

This package contains implementations of efficient representations and updating algorithms for Cholesky factorizations.

Example of an estimation of cell-type proportions using reference-free method (RefFreeEWAS)

This class solves the problem of decomposition of an arbitrary matrix into a series of primitive matrices, which are rotations, scaling and translation. Solves the problem of the lack of mechanisms for working with the skew-component of the matrix, for example, in Unity.

Contains implementations of efficient representations of and updating algorithms for QR factorizations.

The code for prototype selection and instance ranking using matrix decomposition and subspace learning

Numerical Techniques (Matrix Decomposition, Matrix Equation Solvers, Inversion, Iterative Root Finding), All Implemented from scratch in Python

Implementation of the Finite Element Method (FEM) to solve static equilibrium problems using rectangular elements (2D)

Fatoração de Matrizes para Sistemas de Classificação de Machine Learning

Reformulation of SDPs using block factor-width two matrices

Fondements de l’Algorithmique Algébrique. Implémentation de différent algorithme appliqué à des matrices à coefficients dans Z/nZ.

It is one of my three seminars for Master degree in TU Chemnitz

implement machine learning models from scratch