Abstract
ABSTRACTThe rank minimization problem (RMP) asks to find a matrix of lowest rank inside a linear variety of the space of matrices. The low-rank matrix completion (LRMC) problem asks to complete a partially filled matrix such that the resulting matrix has smallest possible rank. The tensor rank problem asks to determine the rank of a tensor. We show that these three problems are equivalent: each one of the problems can be reduced to the other two.
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