Physics and Python stuff. Most of the videos here are either adapted from class lectures or solving physics problems. I really like to use numerical calculations without all the fancy programming ...

It employs the matrix regression model to accept matrix as input and bridges each matrix to its label. Based on the intrinsic property of regression coefficients, we design some sparse constraints on ...

There is increasing interest in the study of community detection for sparse networks. Here, we propose a new method for detecting communities in sparse networks that uses the symmetrized Laplacian ...

Given a sparse matrix A, we define and discuss a matrix B that approximates A–1, has exactly the same sparsity structure as A, and varies contravariantly with A. Unlike many other sparse inverse ...

Discover how nvmath-python leverages NVIDIA CUDA-X math libraries for high-performance matrix operations, optimizing deep learning tasks with epilog fusion, as detailed by Szymon Karpiński.

Improving on this has been an open problem even for sparse linear systems with poly (n) condition number. In this paper, we present an algorithm that solves linear systems in sparse matrices ...

Learn how this popular Python library accelerates math at scale, especially when paired with tools like Cython and Numba.

The array-api and broader data-api consortium generalizes this goal for a variety of array implementations – and has the support of large amounts of the numeric-python ecosystem. Benefits Downstream ...

Recently, the sparse vector code (SVC) is emerging as a promising solution for short-packet transmission in ultra-reliable and low-latency communication (URLLC). In the SVC process, the encoding and ...

Learn some tips and tricks for improving the speed and accuracy of matrix decompositions in R or Python, two popular languages for statistical programming.