Compute or Estimate the Condition Number of a Matrix

Description

The condition number of a regular (square) matrix is the product of the norm of the matrix and the norm of its inverse (or pseudo-inverse), and hence depends on the kind of matrix-norm.

kappa() computes by default (an estimate of) the 2-norm condition number of a matrix or of the R matrix of a QR decomposition, perhaps of a linear fit. The 2-norm condition number can be shown to be the ratio of the largest to the smallest non-zero singular value of the matrix.

rcond() computes an approximation of the reciprocal condition number, see the details.

Usage

kappa(z, ...)
## Default S3 method:
kappa(z, exact = FALSE,
      norm = NULL, method = c("qr", "direct"),
      inv_z = solve(z),
      triangular = FALSE, uplo = "U", ...)

## S3 method for class 'lm'
kappa(z, ...)
## S3 method for class 'qr'
kappa(z, ...)

.kappa_tri(z, exact = FALSE, LINPACK = TRUE, norm = NULL, uplo = "U", ...)

rcond(x, norm = c("O","I","1"), triangular = FALSE, uplo = "U", ...)

Arguments

Details

For kappa(), if exact = FALSE (the default) the condition number is estimated by a cheap approximation to the 1-norm of the triangular matrix R of the qr(x) decomposition z = QR. However, the exact 2-norm calculation (via svd) is also likely to be quick enough.

Note that the approximate 1- and Inf-norm condition numbers via method = "direct" are much faster to calculate, and rcond() computes these reciprocal condition numbers, also for complex matrices, using standard LAPACK routines. Currently, also the kappa*() functions compute these approximations whenever exact is false, i.e., by default.

kappa and rcond are different interfaces to partly identical functionality.

.kappa_tri is an internal function called by kappa.qr and kappa.default; tri is for triangular and its methods only consider the upper or lower triangular part of the matrix, depending on uplo = "U" or "L", where "U" was internally hard wired before R 4.4.0.

Unsuccessful results from the underlying LAPACK code will result in an error giving a positive error code: these can only be interpreted by detailed study of the FORTRAN code.

Value

The condition number, kappa, or an approximation if exact = FALSE.

Author(s)

The design was inspired by (but differs considerably from) the S function of the same name described in Chambers (1992).

Source

The LAPACK routines DTRCON and ZTRCON and the LINPACK routine DTRCO.

LAPACK and LINPACK are from https://netlib.org/lapack/ and https://netlib.org/linpack/ and their guides are listed in the references.

References

Anderson. E. and ten others (1999) LAPACK Users' Guide. Third Edition. SIAM.
Available on-line at https://netlib.org/lapack/lug/lapack_lug.html.

Chambers, J. M. (1992) Linear models. Chapter 4 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.

Dongarra, J. J., Bunch, J. R., Moler, C. B. and Stewart, G. W. (1978) LINPACK Users Guide. Philadelphia: SIAM Publications.

See Also

norm; svd for the singular value decomposition and qr for the QR one.

Examples

kappa(x1 <- cbind(1, 1:10)) # 15.71
kappa(x1, exact = TRUE)     # 13.68
kappa(x2 <- cbind(x1, 2:11)) # high! [x2 is singular!]

hilbert <- function(n) { i <- 1:n; 1 / outer(i - 1, i, `+`) }
sv9 <- svd(h9 <- hilbert(9))$ d
kappa(h9)  # pretty high; by default {exact=FALSE, method="qr"} :
kappa(h9) == kappa(qr.R(qr(h9)), norm = "1")
all.equal(kappa(h9, exact = TRUE), # its definition:
          max(sv9) / min(sv9),
          tolerance = 1e-12) ## the same (typically down to 2.22e-16)
kappa(h9, exact = TRUE) / kappa(h9)  # 0.677 (i.e., rel.error = 32%)

## Exact kappa for rectangular matrix
## panmagic.6npm1(7) :
pm7 <- rbind(c( 1, 13, 18, 23, 35, 40, 45),
             c(37, 49,  5, 10, 15, 27, 32),
             c(24, 29, 41, 46,  2, 14, 19),
             c(11, 16, 28, 33, 38, 43,  6),
             c(47,  3,  8, 20, 25, 30, 42),
             c(34, 39, 44,  7, 12, 17, 22),
             c(21, 26, 31, 36, 48,  4,  9))

kappa(pm7, exact=TRUE, norm="1") # no problem for square matrix

m76 <- pm7[,1:6]
(m79 <- cbind(pm7, 50:56, 63:57))

## Moore-Penrose inverse { ~= MASS::ginv(); differing tol (value & meaning)}:
## pinv := p(seudo) inv(erse)
pinv <- function(X, s = svd(X), tol = 64*.Machine$double.eps) {
    if (is.complex(X))
        s$u <- Conj(s$u)
    dx <- dim(X)
    ## X = U D V' ==> Result =  V {1/D} U'
    pI <- function(u,d,v) tcrossprod(v, u / rep(d, each = dx[1L]))
    pos <- (d <- s$d) > max(tol * max(dx) * d[1L], 0)
    if (all(pos))
        pI(s$u, d, s$v)
    else if (!any(pos))
        array(0, dX[2L:1L])
    else { # some pos, some not:
        i <- which(pos)
        pI(s$u[, i, drop = FALSE], d[i],
           s$v[, i, drop = FALSE])
    }
}

## rectangular
kappa(m76, norm="1")
try( kappa(m76, exact=TRUE, norm="1") )# error in  solve().. must be square

## ==> use pseudo-inverse instead of solve() for rectangular {and norm != "2"}:
iZ <- pinv(m76)
kappa(m76, exact=TRUE, norm="1", inv_z = iZ)
kappa(m76, exact=TRUE, norm="M", inv_z = iZ)
kappa(m76, exact=TRUE, norm="I", inv_z = iZ)

iX <- pinv(m79)
kappa(m79, exact=TRUE, norm="1", inv_z = iX)
kappa(m79, exact=TRUE, norm="M", inv_z = iX)
kappa(m79, exact=TRUE, norm="I", inv_z = iX)