All Linear Algebra Resources
Example Questions
Example Question #41 : Linear Mapping
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 #902 : 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.
Yes.
No; it is neither.
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 #43 : 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.
Yes.
No; it is onto but not one-to-one.
No; it is one-to-one but not onto.
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 #903 : 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?
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 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 #45 : Linear Mapping
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 #41 : Linear Mapping
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.
False
True
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.
Example Question #42 : Linear Mapping
is the set of all two-by-two matrices.
Define the linear mapping as follows:
True or false: is one-to-one and onto.
False; is neither one-to-one nor onto
False; is onto but not one-to-one
False; is one-to-one but not onto
True
True
The domain and the codomain of are identical, so is one to one if and only if it is onto. It suffices to test either condition; so it will be determined whether is onto.
is onto if, for each , there exists such that . Let
Then, if
,
then
.
is onto; it follows that is also one-to-one.
Example Question #43 : Linear Mapping
is the set of all two-by-two matrices.
Define the mapping as follows:
True or false: is a linear mapping.
False
True
True
is a linear mapping if two conditions hold:
Additivity:
For all
Homogeneity:
For all and scalar ,
First, test for additivity.
Let
Then
and ,
and
.
Additivity is satisfied.
Now test for homogeneity. Let be a scalar. Then
.
Homogeneity is satisfied.
is a linear mapping.
Example Question #44 : Linear Mapping
is the set of all polynomials of finite degree in .
Define mapping as follows:
True or false: is a linear mapping.
True
False
False
is a linear mapping if two conditions hold:
Additivity:
For all
Homogeneity:
For all and scalar ,
Homogeneity can be disproved through counterexample.
Let and ..
Then
,
and
However
,
so homogeneity does not hold in general. is not a linear mapping.