Matrix (version 1.2-14)

lu: (Generalized) Triangular Decomposition of a Matrix


Computes (generalized) triangular decompositions of square (sparse or dense) and non-square dense matrices.


lu(x, …)
# S4 method for matrix
lu(x, warnSing = TRUE, …)
# S4 method for dgeMatrix
lu(x, warnSing = TRUE, …)
# S4 method for dgCMatrix
lu(x, errSing = TRUE, order = TRUE, tol = 1,
   keep.dimnames = TRUE, …)



a dense or sparse matrix, in the latter case of square dimension. No missing values or IEEE special values are allowed.


(when x is a "'>denseMatrix") logical specifying if a warning should be signalled when x is singular.


(when x is a "'>sparseMatrix") logical specifying if an error (see stop) should be signalled when x is singular. When x is singular, lu(x, errSing=FALSE) returns NA instead of an LU decomposition. No warning is signalled and the useR should be careful in that case.


logical or integer, used to choose which fill-reducing permutation technique will be used internally. Do not change unless you know what you are doing.


positive number indicating the pivoting tolerance used in cs_lu. Do only change with much care.


logical indicating that dimnames should be propagated to the result, i.e., “kept”. This was hardcoded to FALSE in upto Matrix version 1.2-0. Setting to FALSE may gain some performance.

further arguments passed to or from other methods.


An object of class "LU", i.e., "'>denseLU" (see its separate help page), or "sparseLU", see '>sparseLU; this is a representation of a triangular decomposition of x.


lu() is a generic function with special methods for different types of matrices. Use showMethods("lu") to list all the methods for the lu generic.

The method for class '>dgeMatrix (and all dense matrices) is based on LAPACK's "dgetrf" subroutine. It returns a decomposition also for singular and non-square matrices.

The method for class '>dgCMatrix (and all sparse matrices) is based on functions from the CSparse library. It signals an error (or returns NA, when errSing = FALSE, see above) when the decomposition algorithm fails, as when x is (too close to) singular.


Golub, G., and Van Loan, C. F. (1989). Matrix Computations, 2nd edition, Johns Hopkins, Baltimore.

Timothy A. Davis (2006) Direct Methods for Sparse Linear Systems, SIAM Series “Fundamentals of Algorithms”.

See Also

Class definitions '>denseLU and '>sparseLU and function expand; qr, chol.


Run this code
##--- Dense  -------------------------
x <- Matrix(rnorm(9), 3, 3)
dim(x2 <- round(10 * x[,-3]))# non-square
expand(lu2 <- lu(x2))

##--- Sparse (see more in ?"sparseLU-class")----- % ./sparseLU-class.Rd

pm <- as(readMM(system.file("external/pores_1.mtx",
                            package = "Matrix")),
str(pmLU <- lu(pm))		# p is a 0-based permutation of the rows
                                # q is a 0-based permutation of the columns
## permute rows and columns of original matrix
ppm <- pm[pmLU@p + 1L, pmLU@q + 1L]
pLU <- drop0(pmLU@L %*% pmLU@U) # L %*% U -- dropping extra zeros
## equal up to "rounding"
ppm[1:14, 1:5]
pLU[1:14, 1:5]
# }

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