SparseM.ops {SparseM}R Documentation

Basic Linear Algebra for Sparse Matrices


Basic linear algebra operations for sparse matrices of class matrix.csr.



x matrix of class matrix.csr.
y matrix of class matrix.csr or a dense matrix or vector.
value replacement values.
i,j vectors of elements to extract or replace.
nrow optional number of rows for the result.
lag an integer indicating which lag to use.
differences an integer indicating the order of the difference.


Linear algebra operations for matrices of class matrix.csr are designed to behave exactly as for regular matrices. In particular, matrix multiplication, kronecker product, addition, subtraction and various logical operations should work as with the conventional dense form of matrix storage, as does indexing, rbind, cbind, and diagonal assignment and extraction. The method diag may be used to extract the diagonal of a matrix.csr object, to create a sparse diagonal see SparseM.ontology.

The function det computes the determinant of the argument matrix. If the matrix is of class matrix.csr then it must be symmetric, or an error will be returned. If the matrix is of class matrix.csr.chol then the determinant of the Cholesky factor is returned, ie the product of the diagonal elements.

The function norm is used to check for symmetry by computing the maximum of the elements of the difference between the matrix and its transpose. Optionally, this sup norm can be replaced by the Hilbert-Schmidt norm, or the l1 norm.


Koenker, R and Ng, P. (2002). SparseM: A Sparse Matrix Package for R,

See Also

slm for sparse linear model fitting. SparseM.ontology for coercion and other class relations involving the sparse matrix classes.


n1 <- 10
n2 <- 10
p <- 6
y <- rnorm(n1)
a <- rnorm(n1*p)
a[abs(a)<0.5] <- 0
A <- matrix(a,n1,p)
A.csr <- as.matrix.csr(A)
b <- rnorm(n2*p)
b[abs(b)<1.0] <- 0
B <- matrix(b,n2,p)
B.csr <- as.matrix.csr(B)

# matrix transposition and multiplication

# kronecker product
A.csr %x% matrix(1:4,2,2)

[Package SparseM version 0.54 Index]