gnm (version 0.8-1)

MPinv: Moore-Penrose Pseudoinverse of a Real-valued Matrix

Description

Computes the Moore-Penrose generalized inverse, optionally using a method (appropriate for symmetric matrices only) which exploits the direct inversion of a nonsingular diagonal submatrix if such exists.

Usage

MPinv(mat, eliminate = numeric(0), onlyFirstCol = FALSE,
      onlyNonElim = FALSE, tolerance = 100*.Machine$double.eps,
      rank = NULL, method = "svd")

Arguments

mat

A real matrix, which should be symmetric unless eliminate has zero length.

eliminate

Numeric. A vector of indices identifying rows/columns which form a diagonal submatrix of mat.

onlyFirstCol

Logical. If TRUE, return only the first non-eliminated column of the generalized inverse matrix; otherwise the whole matrix.

onlyNonElim

Logical. If TRUE, return only rows/columns that are in the complement of the set specified in eliminate.

tolerance

A positive scalar which determines the tolerance for detecting zeroes among the singular values.

rank

Either NULL, in which case the rank of mat is determined numerically; or an integer specifying the rank of mat if it is known. No check is made on the validity of any non-NULL value.

method

Character, one of "svd", "chol"

Value

• A matrix, with an additional attribute named "rank" containing the numerically determined rank of the matrix.

Details

Real-valuedness is not checked, neither is symmetry, nor the diagonality of the submatrix when eliminate is used.

When eliminate is used, all of the eliminated submatrix's diagonal entries must be non-zero.

The purpose of the eliminate argument is a substantial reduction of the computational burden when the number of `eliminated' rows/columns is large.

The specification method = "chol" is valid only for symmetric matrices. No check for symmetry is made.

References

Harville, D. A. (1997). Matrix Algebra from a Statistician's Perspective. New York: Springer.

See Also

ginv

Examples

A <- matrix(c(1, 1, 0,
              1, 1, 0,
              2, 3, 4), 3, 3)
B <- MPinv(A)
A %*% B %*% A - A  # essentially zero
B %*% A %*% B - B  # essentially zero
attr(B, "rank")    # here 2

## demonstration that "svd" and "chol" deliver essentially the same
## results for symmetric matrices:
A <- crossprod(A)
MPinv(A) - MPinv(A, method = "chol") ##  (essentially zero) 

## now a symmetric example with diagonal submatrix to `eliminate'
A <- matrix(c(1, 0, 2,
              0, 2, 3,
              2, 3, 4), 3, 3)
B <- MPinv(A, eliminate = 1:2)
A %*% B %*% A - A  # essentially zero
B %*% A %*% B - B  # essentially zero
attr(B, "rank")        # here 3  

## demo that eliminate can give substantial speed gains
A <- diag(rnorm(100))
A <- cbind(A, matrix(rnorm(200), 100, 2))
A <- rbind(A, cbind(t(A[, 101:102]), matrix(c(1, 2, 2, 1), 2, 2)))
system.time(for (i in 1:1000) B <-  MPinv(A))
##  [1] 30.85  0.06 30.91  0.00  0.00
system.time(for (i in 1:1000) B <-  MPinv(A, eliminate = 1:100))
##  [1] 3.10 0.00 3.11 0.00 0.00