matrix-computations

Clean, high-performance Python implementation of fundamental mathematical algorithms: Discrete Mathematics, Calculus, and Linear Algebra

Frobenius Norm Calculator, a linear algebra utility designed to compute the square root of the sum of absolute squared matrix elements. It provides a seamless user experience to help students, mathematicians, and engineers analyze matrix magnitudes, evaluate numerical stability, and perform linear algebra computations.

Structured Matrix Package (LBNL)

Matrices with interval coefficients in Julia

A Lean 4 formalization of fundamental theorems in matrix analysis, including the Courant-Fischer min-max theorem, Singular Value Decomposition (SVD), Frobenius norm properties, and the Eckart-Young theorem.

Verified numerical methods, statistical inference and leakage-aware model evaluation for GNU Octave: omp, a namespaced library with every function tested against an independent reference, research studies with baselines and negative results, and a curriculum of 13 notebooks, four mini-projects and cheat sheets.

Analyze sentiment in PUBG Mobile text using this browser-based NLP tool.

🔍 Compare various direct and iterative methods for solving linear equations in MATLAB to analyze performance and efficiency with practical implementations.

Provides compile-time contraction pattern analysis to determine optimal tensor operation to perform.

References for Console Applications.

High-performance dense linear-algebra kernels in C - a guided ladder of optimizations for matrix × matrix (`matmul`) and matrix × vector (`matvec`) upto the BLAS level for general CPUs

4x4 Matrix math single header library written in C. Supports SSE intrinsics.

Matrix toolbox

fonctions et class de calcule matriciel et vectoriel pour tout type y compris les nombre complexes

A C++20 vector and matrix mathematics library with SIMD acceleration for x86/x64 (SSE/AVX) and ARM (NEON).

High-Performance Python Package for Scalable Randomized Algorithms in Linear Algebra

Matrix Exponential Approximation using CUDA

Python implementation of a linear algebra library built from scratch without NumPy, featuring matrix decompositions, linear system solvers, eigenvalue methods, and vector space computations.

Makine Öğrenmesi için Lineer Cebir ve Matris Hesaplamaları masterclass'ı. Hazır kütüphaneler yok; saf geometrik sezgi ve NumPy var. Sıfırdan Gauss eleme, LU ve QR ayrıştırmaları, kararlı en küçük kareler, özdeğer iterasyonları ve doruk noktası olarak PCA ile uçtan uca SVD tabanlı boyut indirgeme mimarisi.

Multiple precision matrix computations with HODLR representation and mixed-precision simulations