The matrix-variate normal distribution is a popular model for high-dimensional transposable data because it decomposes the dependence structure of the random matrix into the Kronecker product of two ...

Linear Complementarity Problems and Matrix Theory Publication Trend The graph below shows the total number of publications each year in Linear Complementarity Problems and Matrix Theory.

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.

We used a simulation model to evaluate this hypothesis for three different aspects of matrix spatial structure (contrast among matrix cover types, number of matrix cover types, and grain of matrix ...

A trailblazing metasurface computer architecture solves intricate matrix equations orders of magnitude faster than conventional processors by performing complex math intrinsically using scattered ...

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.” ...