# Factor Fitting, Rank Allocation, and Partitioning in Multilevel Low Rank Matrices

CoRR（2023）

摘要

We consider multilevel low rank (MLR) matrices, defined as a row and column
permutation of a sum of matrices, each one a block diagonal refinement of the
previous one, with all blocks low rank given in factored form. MLR matrices
extend low rank matrices but share many of their properties, such as the total
storage required and complexity of matrix-vector multiplication. We address
three problems that arise in fitting a given matrix by an MLR matrix in the
Frobenius norm. The first problem is factor fitting, where we adjust the
factors of the MLR matrix. The second is rank allocation, where we choose the
ranks of the blocks in each level, subject to the total rank having a given
value, which preserves the total storage needed for the MLR matrix. The final
problem is to choose the hierarchical partition of rows and columns, along with
the ranks and factors. This paper is accompanied by an open source package that
implements the proposed methods.

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