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We show that Boolean matrix multiplication, computed as a sum of products of column vectors with row vectors, is essentially the same as Warshalls algorithm for computing the transitive closure matrix of a graph from its adjacency matrix. Warshalls algorithm can be generalized to Floyds algorithm for computing the distance matrix of a graph with weighted edges. We will generalize Boolean matrices in the same way, keeping matrix multiplication essentially equivalent to the Floyd-Warshall algorithm. This way, we get matrices over a semiring, which are similar to the so-called funny matrices. We discuss our implementation of operations on Boolean matrices and on their generalization, which make use of vector instructions.
We present a parallel algorithm for computing the approximate factorization of an $N$-by-$N$ kernel matrix. Once this factorization has been constructed (with $N log^2 N $ work), we can solve linear systems with this matrix with $N log N $ work. Kern
Euclidean distance matrices (EDM) are matrices of squared distances between points. The definition is deceivingly simple: thanks to their many useful properties they have found applications in psychometrics, crystallography, machine learning, wireles
An arbitrary $mtimes n$ Boolean matrix $M$ can be decomposed {em exactly} as $M =Ucirc V$, where $U$ (resp. $V$) is an $mtimes k$ (resp. $ktimes n$) Boolean matrix and $circ$ denotes the Boolean matrix multiplication operator. We first prove an exact
Euclidean distance matrices (EDMs) are a major tool for localization from distances, with applications ranging from protein structure determination to global positioning and manifold learning. They are, however, static objects which serve to localize
In this paper, the interval-valued intuitionistic fuzzy matrix (IVIFM) is introduced. The interval-valued intuitionistic fuzzy determinant is also defined. Some fundamental operations are also presented. The need of IVIFM is explain by an example.