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BUILT-IN MATHEMATICA SYMBOL
Advanced Matrix Operations
Tutorials »
|
Inverse
LeastSquares
Fit
SingularValueDecomposition
SingularValueList
See Also »
|
Linear Systems
Matrices and Linear Algebra
Matrix-Based Minimization
New in 8.0: Mathematics & Algorithms
More About »
PseudoInverse
PseudoInverse
[
m
]
finds the pseudoinverse of a rectangular matrix.
MORE INFORMATION
PseudoInverse
works on both symbolic and numerical matrices.
For a square matrix,
PseudoInverse
gives the Moore-Penrose inverse.
For numerical matrices,
PseudoInverse
is based on
SingularValueDecomposition
.
PseudoInverse
[
m
,
Tolerance
->
t
]
specifies that singular values smaller than
t
times the maximum singular value should be dropped.
With the default setting
Tolerance
->
Automatic
, singular values are dropped when they are less than 100 times
, where
p
is
Precision
[
m
]
.
For non-singular square matrices
M
, the pseudoinverse
is equivalent to the standard inverse.
EXAMPLES
CLOSE ALL
Basic Examples
(1)
A matrix has a pseudoinverse even if it is singular:
A matrix has a pseudoinverse even if it is singular:
In[1]:=
Out[1]=
Scope
(2)
m
is a 4×3 matrix:
Compute using exact arithmetic:
Compute using machine arithmetic:
Compute using 24-digit precision arithmetic:
Compute the pseudoinverse for a random complex 3×2 matrix:
Generalizations & Extensions
(1)
Compute symbolic result:
Options
(1)
m
is a 16×16 Hilbert matrix:
Some singular values are below the default tolerance for machine precision:
Compute the pseudoinverse with the default tolerance:
It is not a true inverse since some singular values were considered to be effectively zero:
Compute the pseudoinverse with no tolerance:
Even though no singular values were considered zero, it is worse due to numerical error:
Applications
(1)
Here is some data:
Construct a design matrix for fitting to a line:
Get the coefficients for a linear least-squares fit:
This is the same as the result given by
Fit
:
Properties & Relations
(3)
For a nonsingular matrix, the pseudoinverse is the same as the inverse:
For
,
gives the minimum norm
x
that minimizes
:
Adding any vector in the
NullSpace
of
m
will leave the residual unchanged:
The minimum is at
:
PseudoInverse
satisfies the Moore-Penrose equations []:
SEE ALSO
Inverse
LeastSquares
Fit
SingularValueDecomposition
SingularValueList
TUTORIALS
Advanced Matrix Operations
MORE ABOUT
Linear Systems
Matrices and Linear Algebra
Matrix-Based Minimization
New in 8.0: Mathematics & Algorithms
RELATED LINKS
NKS|Online
(
A New Kind of Science
)
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