All Linear Algebra Resources
Example Questions
Example Question #31 : Linear Mapping
refers to the set of all functions that are continuous on
.
Define the linear mapping as follows:
True or false: is in the kernel of
.
False
True
False
The kernel of a linear transformation is the subset of the domain of
that maps into the zero of its range, so, by definition,
if and only if
To determine whether this is true or false, evaluate the integral:
, so
.
Example Question #892 : Linear Algebra
refers to the set of all functions that are continuous on
.
Define the linear mapping as follows:
True or false: is in the kernel of
.
False
True
True
The kernel of a linear transformation is the subset of the domain of
that maps into the zero of its range. It follows that
if and only if
To determine whether this is true or false, evaluate the integral:
Therefore, .
Example Question #34 : Linear Mapping
True or false: If is a linear mapping, and
is a vector space, then
is a subspace of
.
False
True
False
For example, if is the space of all vectors in
of the form
, and
is the space of all vectors in
the form
, then
is a linear mapping, but
is not a subset of
, let alone a subspace of
.
Example Question #901 : Linear Algebra
True or false: The identity mapping , is also considered a linear mapping, regardless of the vector space
.
True
False
True
Verifying the conditions for a linear mapping, we have
Hence the identity mapping is closed under vector addition and scalar multiplication, and is therefore a linear mapping.
Example Question #41 : Linear Mapping
is the set of all polynomials with degree
or less.
Define a linear mapping as follows:
Is this mapping one-to-one and onto?
No; it is neither.
No; it is onto but not one-to-one.
No; it is one-to-one but not onto.
Yes.
No; it is one-to-one but not onto.
is a vector space of dimension
.
is a linear mapping of a four-dimensional vector space into a five-dimensional vector space; since the range has greater dimension than the domain,
cannot be onto. It remains to be determined whether it is one-to-one.
A transformation is one-to-one if and only if for any in the domain,
implies that
.
Suppose that for some
.
Since are third-degree polynomials:
for some scalar .
, so
For these two polynomials to be equal, it must hold that all coefficients of the terms of equal degree are equal. We can immediately see from the first-degree terms that . Applying some simple algebra, it follows almost as quickly that
,
, and
. Thus,
, and
is one-to-one.
The correct response is that is one-to-one but not onto.
Example Question #901 : Linear Algebra
is the set of all polynomials with degree
or less.
Define a linear mapping as follows:
Is this mapping one-to-one and onto?
No; it is onto but not one-to-one.
No; it is one-to-one but not onto.
No; it is neither.
Yes.
Yes.
is a linear mapping of a vector space into itself, so it is possible for
to be both one-to-one and onto.
A transformation is one-to-one if and only if for any in the domain,
implies that
.
Suppose that for some
.
Since are third-degree polynomials:
for some scalar .
, so
For these two polynomials to be equal, it must hold that all coefficients of the terms of equal degree are equal. We can immediately see from the first-degree terms that . Applying some simple algebra, it follows almost as quickly that
,
, and
. Thus,
, and
is one-to-one.
A transformation is onto if, for each in the range, there exists
in the domain such that
.
Let . Then
for some scalar .
If , then, if
is defined as before,
Therefore,
,
,
,
Or,
,
,
,
.
Thus,
is the polynomial in
that
maps into
. Since such a polynomial exists in the domain for each range element, it follows that
is onto.
The correct response is that is one-to-one and onto.
Example Question #901 : Linear Algebra
is the set of all real polynomials with degree 3 or less.
Define the linear mapping as follows:
Is this linear mapping one-to-one and onto?
No; it is one-to-one but not onto.
No; it is onto but not one-to-one.
Yes.
No; it is neither.
No; it is onto but not one-to-one.
is a linear mapping of a four-dimensional vector space into a one-dimensional vector space; it cannot be one-to-one.
is onto if, for every element
in its codomain, which here is
, there exists at least one
in the domain
so that
.
For each , we can choose the constant polynomial
, so each
has at least one domain element that maps into it.
is onto but not one-to-one.
Example Question #522 : Operations And Properties
is the set of all polynomials with degree 3 or less.
Define the linear mapping as follows:
Is this linear mapping one-to-one and onto?
Yes
No; it is one-to-one but not onto.
No; it is neither.
No; it is onto but not one-to-one.
No; it is neither.
A linear mapping is one-to-one if, for every
in the domain such that
, it must follow that
. We can show that
is not one-to-one by finding
such that
. For example, let
Then
Since there exists such that
,
is not one-to-one.
is onto if, for every element
in its codomain, which here is
, there exists at least one
in the domain
so that
.
Suppose . To find
such that
, set:
for some constant
.
However, .
Therefore, there does not exist such that
.
The correct response is that is not one-to-one or onto.
Example Question #522 : Operations And Properties
is the set of all polynomials with degree
or less;
Define a transformation as follows:
True or false: is an example of a linear mapping.
True
False
True
is a linear mapping if and only if the following two conditions hold:
Additivity: for all
,
Homogeneity: for all
scalar.
We know from calculus that both properties hold for any definite integrals, so
and
This makes a linear mapping.
Example Question #905 : Linear Algebra
is the set of all polynomials with degree 3 or less.
Define a transformation as follows:
.
True or false: is an example of a linear mapping.
False
True
True
is a linear mapping if and only if the following two conditions hold:
Additivity: for all
,
Homogeneity: for all
scalar.
Let . Then
By distribution:
By the sum rule of derivatives:
Thus,
,
proving that additivity holds.
Let and
be a scalar. Then
By the scalar product rule of derivatives,
,
and
,
proving that homogeneity holds.
is a linear mapping.
Certified Tutor
Certified Tutor
All Linear Algebra Resources
