matrixcalc (version 1.0-3)

is.positive.definite: Test matrix for positive definiteness

Description

This function returns TRUE if the argument, a square symmetric real matrix x, is positive definite.

Usage

is.positive.definite(x, tol=1e-8)

Arguments

x

a matrix

tol

a numeric tolerance level

Value

TRUE or FALSE.

Details

For a positive definite matrix, the eigenvalues should be positive. The R function eigen is used to compute the eigenvalues. If any of the eigenvalues in absolute value is less than the given tolerance, that eigenvalue is replaced with zero. If any of the eigenvalues is less than or equal to zero, then the matrix is not positive definite. Otherwise, the matrix is declared to be positive definite.

References

Bellman, R. (1987). Matrix Analysis, Second edition, Classics in Applied Mathematics, Society for Industrial and Applied Mathematics.

See Also

is.positive.semi.definite, is.negative.definite, is.negative.semi.definite, is.indefinite

Examples

# NOT RUN {
###
### identity matrix is always positive definite
I <- diag( 1, 3 )
is.positive.definite( I )
###
### positive definite matrix
### eigenvalues are 3.4142136 2.0000000 0.585786
###
A <- matrix( c( 2, -1, 0, -1, 2, -1, 0, -1, 2 ), nrow=3, byrow=TRUE )
is.positive.definite( A )
###
### positive semi-defnite matrix
### eigenvalues are 4.732051 1.267949 8.881784e-16
###
B <- matrix( c( 2, -1, 2, -1, 2, -1, 2, -1, 2 ), nrow=3, byrow=TRUE )
is.positive.definite( B )
###
### negative definite matrix
### eigenvalues are -0.5857864 -2.0000000 -3.4142136
###
C <- matrix( c( -2, 1, 0, 1, -2, 1, 0, 1, -2 ), nrow=3, byrow=TRUE )
is.positive.definite( C )
###
### negative semi-definite matrix
### eigenvalues are 1.894210e-16 -1.267949 -4.732051
###
D <- matrix( c( -2, 1, -2, 1, -2, 1, -2, 1, -2 ), nrow=3, byrow=TRUE )
is.positive.definite( D )
###
### indefinite matrix
### eigenvalues are 3.828427  1.000000 -1.828427
###
E <- matrix( c( 1, 2, 0, 2, 1, 2, 0, 2, 1 ), nrow=3, byrow=TRUE )
is.positive.definite( E )
# }