In standard clustering problems, data points are represented by vectors, and by stacking them together, one forms a data matrix with row or column cluster structure. In this paper, we consider a class of binary matrices, arising in many applications, ...
During the iterations of interior point methods symmetric indefinite systems are decomposed by LD L T factorization. This step can be performed in a special way where the symmetric indefinite system is transformed to a positive definite one, ...
The effectiveness of sparse matrix techniques for directly solving large-scale linear least-squares problems is severely limited if the system matrix A has one or more nearly dense rows. In this paper, we partition the rows of A into sparse rows and ...
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