Generalize to
matrices
You've seen that each
matrix
has an orthonormal basis
which it stretches and sends to an orthonormal basis
.
Analogous facts hold in higher dimensions.
A
matrix
has an orthonormal basis
for
which it stretches and sends to an orthonormal basis
for
.
A
matrix
has an orthonormal basis
for
which it stretches and sends to an orthonormal basis
of
. (The vector
has singular value
and is sent to the zero vector
.)
A
matrix
has an orthonormal basis
for
which it stretches and sends to the first two vectors of an orthonormal basis
of
.
Column space and nullspace.
For a
matrix
you saw that the column space of
was all multiples of
and that the nullspace of
was all multiples of
.
In general for an
matrix
, the vectors
with non-zero singular values are a basis for the column space of
and the vectors
with zero singular values are a basis for the nullspace of
.
Suppose a
matrix
has singular values
,
, and
.
The singular value of
means that
sends multiples of
to the zero vector and that
sends every
to the plane spanned by
and
.
The set
is a basis for the column space and the set
is a basis for the nullspace.
Modified three steps to solving
.
Step 1: Find
so that
.
Since
, the
portion of
is inaccessible.
Throw away the
portion and keep just
.
Continue solving
.
Step 2: Divide by the singular values:
.
Step 3: Use these numbers to form
.
The vector
is a solution to
and a least squares solution to
.
Since
, the general least squares solution to
is
.
(Q42) Suppose
. In terms of the
and or
:
a. Give a basis for the nullspace of
.
b. Give a basis for the column space of
.
c. Give the general least squares solution to
.
Copyright © 2007 Todd Will
Created by
Mathematica (April 15, 2007)