Package 'sparseLRMatrix'

Check the dimension of a sparseLRMatrix

Description

Check the dimension of a sparseLRMatrix

Usage

## S4 method for signature 'sparseLRMatrix'
dim(x)

Arguments

x	A sparseLRMatrix object.

Value

Dimension of x.

Examples

set.seed(528491)

n <- 50
m <- 40
k <- 3

A <- rsparsematrix(n, m, 0.1)

U <- Matrix(rnorm(n * k), nrow = n, ncol = k)
V <- Matrix(rnorm(m * k), nrow = m, ncol = k)

# construct the matrix, which represents A + U %*% t(V)
X <- sparseLRMatrix(sparse = A, U = U, V = V)

dim(X)

s <- svds(X, 5)  # efficient

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Create a sparse plus low rank matrix

Description

Create a sparse plus low rank matrix

Usage

sparseLRMatrix(sparse, U, V)

Arguments

sparse	sparseMatrix.
U	Matrix.
V	Matrix.

Value

A sparseLRMatrix S4 object.

Examples

set.seed(528491)

n <- 50
m <- 40
k <- 3

A <- rsparsematrix(n, m, 0.1)

U <- Matrix(rnorm(n * k), nrow = n, ncol = k)
V <- Matrix(rnorm(m * k), nrow = m, ncol = k)

# construct the matrix, which represents A + U %*% t(V)
X <- sparseLRMatrix(sparse = A, U = U, V = V)

dim(X)

s <- svds(X, 5)  # efficient

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Sparse plus low rank matrix

Description

Eventually this class will subclass Matrix objects, but for now this is a basic implementation that essentially only supports singular value decomposition.

Details

To learn more about S4 classes, please see https://adv-r.hadley.nz/s4.html.

Slots

sparse

sparseMatrix.

U

Matrix.

V

Matrix.

Examples

set.seed(528491)

n <- 50
m <- 40
k <- 3

A <- rsparsematrix(n, m, 0.1)

U <- Matrix(rnorm(n * k), nrow = n, ncol = k)
V <- Matrix(rnorm(m * k), nrow = m, ncol = k)

# construct the matrix, which represents A + U %*% t(V)
X <- sparseLRMatrix(sparse = A, U = U, V = V)

dim(X)

s <- svds(X, 5)  # efficient

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Truncated singular value decomposition of a matrix

Description

A thin wrapper around RSpectra::svds(), please see more detailed documentation there. In particular, this function leverages the function interface.

Usage

## S3 method for class 'sparseLRMatrix'
svds(A, k, nu = k, nv = k, opts = list(), ...)

Arguments

A	Matrix to decompose.
k	Number of singular values to estimate.
nu	Number of left singular vectors to estimate.
nv	Number of right singular vectors to estimate.
opts	Passed to RSpectra::svds().
...	Passed to RSpectra::svds().

Value

A list with the following components:

d	A vector of the computed singular values.
u	An m by nu matrix whose columns contain the left singular vectors. If nu == 0, NULL will be returned.
v	An n by nv matrix whose columns contain the right singular vectors. If nv == 0, NULL will be returned.
nconv	Number of converged singular values.
niter	Number of iterations used.
nops	Number of matrix-vector multiplications used.

Examples

set.seed(528491)

n <- 50
m <- 40
k <- 3

A <- rsparsematrix(n, m, 0.1)

U <- Matrix(rnorm(n * k), nrow = n, ncol = k)
V <- Matrix(rnorm(m * k), nrow = m, ncol = k)

X <- sparseLRMatrix(sparse = A, U = U, V = V)

svds(X, 5)

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