Matrix splitting iteration methods have emerged as potent tools in addressing complementarity problems, which frequently arise in optimisation, economics and engineering applications.

Shurong Zheng, Zhao Chen, Hengjian Cui, Runze Li, HYPOTHESIS TESTING ON LINEAR STRUCTURES OF HIGH-DIMENSIONAL COVARIANCE MATRIX, The Annals of Statistics, Vol. 47, No. 6 (December 2019), pp. 3300-3334 ...

It has a whole collection of sparse matrix solvers. The one I used was CHOLMOD, which is the one used in MatLab IIRC. Which solver to choose depends on the nature of your matrix.

Matrix-variate Gaussian graphical models (GGM) have been widely used for modeling matrix-variate data. Since the support of sparse precision matrix represents the conditional independence graph among ...

Although every business problem can't be solved with D&A, there are some specific use cases for which it can help in deriving insights and improving business performance.

Matrix multiplication advancement could lead to faster, more efficient AI models At the heart of AI, matrix math has just seen its biggest boost "in more than a decade.” ...