svd_problem: Define an SVD problem.

In eigencore: Certified Partial Eigenvalue and Singular Value Computation

Define an SVD problem.

Description

Define an SVD problem.

Usage

svd_problem(A, domain = NULL, codomain = NULL, target = largest())

Arguments

A	Matrix or operator defining the rectangular linear map.
domain	Optional domain-space descriptor.
codomain	Optional codomain-space descriptor.
target	Eigencore singular-value target descriptor.

Value

An eigencore_svd_problem object containing the operator, domain and codomain descriptors, and singular-value target consumed by plan_solver() and solve().

Examples

set.seed(1)
X <- matrix(rnorm(40), 8, 5)
S <- svd_problem(X, target = largest())
fit <- solve(S, rank = 2)
values(fit)

eigencore documentation built on July 26, 2026, 1:06 a.m.