matrixcalc (version 1.0-6)

is.positive.semi.definite: Test matrix for positive semi-definiteness

Description

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

Usage

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

Value

TRUE or FALSE.

Arguments

x

a matrix

tol

a numeric tolerance level

Author

Frederick Novomestky

Details

For a positive semi-definite matrix, the eigenvalues should be non-negative. The R function eigen is used to compute the eigenvalues. If any of the eigenvalues is less than zero, then the matrix is not positive semi-definite. Otherwise, the matrix is declared to be positive semi-definite.

References

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

See Also

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

Examples

###
### identity matrix is always positive definite
I <- diag( 1, 3 )
is.positive.semi.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.semi.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.semi.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.semi.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.semi.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.semi.definite( E )