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.

Estimating the dependence structure in the data is a key task when analyzing compositional data. Real-world compositional data sets are often complex owing to high-dimensionality, heavy tails, and the ...

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 ...

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.” ...