volnmf_simplex_row

Numeric Matrices. Matrices involved in the objective function.

Numeric Matrices. Matrices involved in the objective function. Matrix <code>C.prev</code> serves as initialization. (default=NULL)

C.prev

An integer 0 or 1. Require equality (<code>meq=1</code>) or inequality (<code>meq=0</code>) constratint on rows (by default 1).

<code>volnmf_simplex_row</code> finds non-negative matrix <code>C</code> that minimizes the objective <code>||X-C*R||^2</code>
under constraints that rows of C equal to 1 using per-row quadratic programming.

Implements a set of routines to perform structured matrix factorization with minimum volume constraints. The NMF procedure decomposes a matrix X into a product C * D. Given conditions such that the matrix C is non-negative and has sufficiently spread columns, then volume minimization of a matrix D delivers a correct and unique, up to a scale and permutation, solution (C, D). This package provides both an implementation of volume-regularized NMF and "anchor-free" NMF, whereby the standard NMF problem is reformulated in the covariance domain. This algorithm was applied in Vladimir B. Seplyarskiy Ruslan A. Soldatov, et al. "Population sequencing data reveal a compendium of mutational processes in the human germ line". Science, 12 Aug 2021. <doi:10.1126/science.aba7408>. This package interacts with data available through the 'simulatedNMF' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/vrnmf>. The size of the 'simulatedNMF' package is approximately 8 MB.

Evan Biederstedt

volnmf_simplex_row: Update of a matrix in NMF with equality contstraints on rows.

Description

Usage

Arguments

Value