DLA Discovery guide

One way to obtain a basis for a space (and hence to determine its dimension) is to assign parameters — then each independent parameter corresponds to a basis vector.

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For example, in

R^{2}

we have natural parameters associated to the

x

- and

y

-coordinates:

x = (x, y) .

Expanding this into a linear combination, we get

x = x (1, 0) + y (0, 1) .

Parameter

x

corresponds to vector

(1, 0)

and parameter

y

corresponds to vector

(0, 1),

and together the two corresponding vectors form a basis

{(1, 0), (0, 1)}

for

R^{2} .

(In fact, the standard basis for

R^{2}!

). Since there were two independent parameters required to described an arbitrary vector in the space, this led to two basis vectors, and so the dimension of

R^{2}

is (surprise!)

2 .

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Step-by-step procedure.

Express arbitrary elements in the space in terms of parameters.
Use any extra conditions to reduce to the minimum number of independent parameters (if necessary).
Split up your parametric vector description into a linear combination based on the remaining parameters.
Extract the basis vector attached to each parameter.
Count the basis vectors to determine the dimension of the space (which should also correspond to the number of independent parameters required).

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Discovery 19.2.

In each of the following, determine a basis for the given space using the parameter method outlined above, similarly to the provided

R^{2}

example. Then count the dimension of the space.

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(a)

R^{3} .

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(b)

The subspace of

R^{3}

consisting of vectors whose second coordinate is zero.

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(c)

The subspace of

R^{3}

consisting of vectors whose first and third coordinates are equal.

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(d)

M_{2} (R),

i.e. the space of

2 \times 2

matrices.

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(e)

The subspace of

M_{2} (R)

consisting of upper-triangular matrices.

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(f)

The subspace of

M_{2} (R)

consisting of upper-triangular matrices whose diagonal entries add to zero.

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(g)

The subspace of

M_{2} (R)

consisting of matrices whose entries sum to zero.

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(h)

P_{5} (R),

i.e. the space of polynomials of degree

5

or less.

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(i)

The subspace of

P_{5} (R)

consisting of polynomials with constant term equal to zero.

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(j)

The subspace of

P_{5} (R)

consisting of odd polynomials, i.e. those involving only odd powers of

x

(and no constant term).

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(k)

The subspace of

P_{5} (R)

consisting of even polynomials, i.e. those involving only even powers of

x

(and a constant term).

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A vector space is called finite-dimensional if it can be spanned by a finite set; otherwise, it is called infinite-dimensional. For example,

R^{n}

is finite-dimensional for each value of

n,

because it can be spanned by the finite set of standard basis vectors

{e_{1}, e_{2}, \dots, e_{n}} .

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Discovery 19.3.

Is the vector space of all polynomials is finite- or infinite-dimensional?

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Hint.

S

is a finite set of polynomials, what are the possible degrees of the polynomials in

Span S ?

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We’ve already seen that a linearly dependent spanning set can be reduced to a basis (Proposition 18.5.1). Working the other way, we will use Proposition 17.5.6 to argue in Subsection 19.5.2 that a linearly independent set that is not a spanning set can be built up to a basis by including additional vectors (Proposition 19.5.4). Proposition 17.5.6 tells us exactly how to do this: to ensure linear independence at each step, the new vector to be included should not be in the span of the old (i.e. the new should not be any linear combination of the old).

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Discovery 19.4.

In each of the following, enlarge the provided linearly independent set into a basis for the space.

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Hint: Since we now know the dimensions of these spaces, we know how many linearly independent vectors are required to form a basis. Just guess simple new vectors to include in the given set, one at a time, and for each make sure your new vector is not a linear combination of the vectors you already have. (You can check this by trying to solve an appropriate system of linear equations.)

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(a)

V = R^{3},

S = {(1, 1, 0), (1, 0, 1)} .

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(b)

V = M_{2} (R),

S = {[\begin{matrix} 1 & 1 \\ 1 & 1 \end{matrix}], [\begin{matrix} 1 & 0 \\ 1 & - 1 \end{matrix}]} .

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Discovery 19.5.

Suppose

V

is a finite-dimensional vector space, and

W

is a subspace of

V .

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(a)

What is the relationship between

\dim W

and

\dim V ?

Justify your answer in terms of the definition of dimension.

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Hint.

The pattern of the previous exercise, where a linearly independent set can be built up into a basis, might help in articulating your justification.

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(b)

Is it possible for

\dim W = \dim V

to be true?

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Discovery guide 19.1 Discovery guide A4US

Recall.

Discovery 19.1.

(a)

(b)

(i)

(ii)

(iii)

(c)

(i)

(ii)

(d)

(e)

(i)

(ii)

(iii)

(f)

(i)

(ii)

(g)

(h)

(i)

(ii)

(iii)

(iv)

Step-by-step procedure.

Discovery 19.2.

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

(j)

(k)

Discovery 19.3.

Discovery 19.4.

(a)

(b)

Discovery 19.5.

(a)

(b)

Discovery guide 19.1 Discovery guide
A4 US