simpute.svd: rdname softImpute-internal

In softImpute: Matrix Completion via Iterative Soft-Thresholded SVD

rdname softImpute-internal

Description

rdname softImpute-internal

Usage

simpute.svd(
  x,
  J = 2,
  thresh = 1e-05,
  lambda = 0,
  maxit = 100,
  trace.it = FALSE,
  warm.start = NULL,
  ...
)

Arguments

x	An m by n matrix with NAs. For large matrices can be of class "Incomplete", in which case the missing values are represented as pseudo zeros leading to dramatic storage reduction. x can have been centered and scaled via biScale, and this information is carried along with the solution.
J	Trevor to document this param
thresh	convergence threshold, measured as the relative change in the Frobenius norm between two successive estimates.
lambda	nuclear-norm regularization parameter. If lambda=0, the algorithm reverts to "hardImpute", for which convergence is typically slower, and to local minimum. Ideally lambda should be chosen so that the solution reached has rank slightly less than rank.max. See also lambda0() for computing the smallest lambda with a zero solution.
maxit	maximum number of iterations.
trace.it	with trace.it=TRUE, convergence progress is reported.
warm.start	an svd object can be supplied as a warm start. This is particularly useful when constructing a path of solutions with decreasing values of lambda and increasing rank.max. The previous solution can be provided directly as a warm start for the next.
...	sink argument for unwanted arguments

softImpute documentation built on June 10, 2025, 9:10 a.m.