1.5 Linear Dependence and Linear Independence – Linear Algebra, 5th Edition

1.5 Linear Dependence and Linear Independence

Suppose that V is a vector space over an infinite field and that W is a subspace of V. Unless W is the zero subspace, W is an infinite set. It is desirable to find a “small” finite subset S of W that generates W because we can then describe each vector in W as a linear combination of the finite number of vectors in S. Indeed, the smaller S is, the fewer the number of computations required to represent vectors in W as such linear combinations. Consider, for example, the subspace W of R3 generated by S={u1, u2, u3, u4}, where u1=(2,1, 4), u2=(1,1, 3), u3=(1, 1,1), and u4=(1,2,1). Let us attempt to find a proper subset of S that also generates W. The search for this subset is related to the question of whether or not some vector in S is a linear combination of the other vectors in S. Now u4 is a linear combination of the other vectors in S if and only if there are scalars a1, a2, and a3 such that

u4=a1u1+a2u2+a3u3,

that is, if and only if there are scalars a1, a2, and a3 satisfying

(1,2,1)=(2a1+a2+a3,a1a2+a3, 4a1+3a2a3).

Thus u4 is a linear combination of u1, u2, and u3 if and only if the system of linear equations

2a1+a2+a3=1a1a2+a3=24a1+3a2a3=1

has a solution. The reader should verify that no such solution exists. This does not, however, answer our question of whether some vector in S is a linear combination of the other vectors in S. It can be shown, in fact, that u3 is a linear combination of u1, u2, and u4, namely, u3=2u13u2+0u4.

In the preceding example, checking that some vector in S is a linear combination of the other vectors in S could require that we solve several different systems of linear equations before we determine which, if any, of u1, u2, u3, and u4 is a linear combination of the others. By formulating our question differently, we can save ourselves some work. Note that since u3=2u13u2+0u4, we have

2u1+3u2+u30u4=0.

That is, because some vector in S is a linear combination of the others, the zero vector can be expressed as a linear combination of the vectors in S using coefficients that are not all zero. The converse of this statement is also true: If the zero vector can be written as a linear combination of the vectors in S in which not all the coefficients are zero, then some vector in S is a linear combination of the others. For instance, in the example above, the equation 2u1+3u2+u30u4=0 can be solved for any one of u1, u2, or u3 because each of these has a nonzero coefficient. Therefore any one of u1, u2, or u3 can be written as a linear combination of the other three vectors. Thus, rather than asking whether some vector in S is a linear combination of the other vectors in S, it is more efficient to ask whether the zero vector can be expressed as a linear combination of the vectors in S with coefficients that are not all zero. This observation leads us to the following definition.

Definition.

A subset S of a vector space V is calledlinearly dependent if there exist a finite number of distinct vectorsu1, u2, , un in S and scalars a1, a2, , an, not all zero, such that

a1u1+a2u2++anun=0.

In this case we also say that the vectors of S are linearly dependent.

For any vectors u1, u2, , un, we have a1u1+a2u2++anun=0 if a1=a2==an=0. We call this the trivial representation of 0 as a linear combination of u1, u2, , un. Thus, for a set to be linearly dependent, there must exist a nontrivial representation of 0 as a linear combination of vectors in the set. Consequently, any subset of a vector space that contains the zero vector is linearly dependent, because 0=10 is a nontrivial representation of 0 as a linear combination of vectors in the set.

Example 1

Consider the set

S={(1, 3,4, 2), (2, 2,4, 0), (1,3, 2,4), (1, 0, 1, 0)}

in R4. We show that S is linearly dependent and then express one of the vectors in S as a linear combination of the other vectors in S. To show that S is linearly dependent, we must find scalars a1, a2, a3, and a4, not all zero, such that

a1(1, 3,4, 2)+a2(2, 2,4, 0)+a3(1,3, 2,4)+a4(1, 0, 1, 0)=0.

Finding such scalars amounts to finding a nonzero solution to the system of linear equations

a1+2a2+a3a4=03a1+2a23a3=04a14a2+2a3+a4=02a14a3=0.

One such solution is a1=4, a2=3, a3=2, and a4=0. Thus S is a linearly dependent subset of R4, and

4(1, 3,4, 2)3(2, 2,4, 0)+2(1,3, 2,4)+0(1, 0, 1, 0)=0.

Hence

(1, 3,4, 2)=34(2, 2,4, 0)12(1,3, 2,4)+0(1, 0, 1, 0).

Example 2

In M2×3(R), the set

{(132405), (374627), (2311132)}

is linearly dependent because

5(132405)+3(374627)2(2311132)=(000000).

Definition.

A subset S of a vector space that is not linearly dependent is called linearly independent. As before, we also say that the vectors of S are linearly independent.

The following facts about linearly independent sets are true in any vector space.

  1. The empty set is linearly independent, for linearly dependent sets must be nonempty.

  2. A set consisting of a single nonzero vector is linearly independent. For if {u} is linearly dependent, then au=0 for some nonzero scalar a. Thus

    u=a1(au)=a10=0.
  3. A set is linearly independent if and only if the only representations of 0 as linear combinations of its vectors are trivial representations.

The condition in item 3 provides a useful method for determining whether a finite set is linearly independent. This technique is illustrated in the examples that follow.

Example 3

To prove that the set

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

is linearly independent, we must show that the only linear combination of vectors in S that equals the zero vector is the one in which all the coefficients are zero. Suppose that a1 ,a2 ,a3, and a4 are scalars such that

a1(1, 0, 0,1)+a2(0, 1, 0,1)+a3(0, 0, 1,1)+a4(0, 0, 0, 1)=(0, 0, 0, 0).

Equating the corresponding coordinates of the vectors on the left and the right sides of this equation, we obtain the following system of linear equations.

a1=0a2=0a3=0a1a2a3+a4=0

Clearly the only solution to this system is a1=a2=a3=a4=0, and so S is linearly independent.

Example 4

For k=0, 1, , n let pk(x)=xk+xk+1++xn. The set

{p0(x), p1(x), , pn(x)}

is linearly independent in Pn(F). For if

a0p0(x)+a1p1(x)++anpn(x)=0

for some scalars a0, a1, , an, then

a0+(a0+a1)x+(a0+a1+a2)x2++(a0+a1++an)xn=0.

By equating the coefficients of xk on both sides of this equation for k=1, 2, , n, we obtain

a0=0a0+a1=0a0+a1+a2=0a0+a1+a2++an=0.

Clearly the only solution to this system of linear equations is a0=a1==an=0.

The following important results are immediate consequences of the definitions of linear dependence and linear independence.

Theorem 1.6.

Let V be a vector space, and let S1S2V. If S1 is linearly dependent, then S2 is linearly dependent.

Proof. Exercise.

Corollary.

Let V be a vector space, and let S1S2V. If S2 is linearly independent, then S1 is linearly independent.

Proof. Exercise.

Earlier in this section, we remarked that the issue of whether S is a minimal generating set for its span (that is, one such that no proper subset of S is a generating set) is related to the question of whether some vector in S is a linear combination of the other vectors in S. Thus the issue of whether S is the smallest generating set for its span is related to the question of whether S is linearly dependent. To see why, consider the subset S={u1, u2, u3, u4} of R3, where u1=(2,1, 4), u2=(1,1, 3), u3=(1, 1,1), and u4=(1,2,1). We have previously noted that S is linearly dependent; in fact,

2u1+3u2+u30u4=0.

This equation implies that u3 (or alternatively, u1 or u2) is a linear combination of the other vectors in S. For example, u3=2u13u2+0u4. Therefore every linear combination a1u1+a2u2+a3u3+a4u4 of vectors in S can be written as a linear combination of u1, u2, and u4:

a1u1+a2u2+a3u3+a4u4=a2u1+a2u2+a3(2u13u2+0u4)+a4u4=(a1+2a3)u1+(a23a3)u2+a4u4.

Thus the subset S={ u1, u2, u4 } of S has the same span as S!

More generally, suppose that S is any linearly dependent set containing two or more vectors. Then some vector vS can be written as a linear combination of the other vectors in S, and the subset obtained by removing v from S has the same span as S. It follows that if no proper subset of S generates the span of S, then S must be linearly independent. Another way to view the preceding statement is given in Theorem 1.7.

Theorem 1.7.

Let S be a linearly independent subset of a vector space v, and let v be a vector in V that is not in S. Then S{v} is linearly dependent if and only if vspan(S).

Proof. If S{v} is linearly dependent, then there are vectors u1, u2, , un in S{v} such that a1u1+a2u2+anun=0 for some nonzero scalars a1, a2, , an. Since S is linearly independent, one of the ui’s, say u1, equals v. Thus a1v+a2u2++anun=0, and so

v=a11(a2u2anun)=(a11a2)u2(a11an)un.

Because v is a linear combination of u2,,un, which are in S, we have vspan(S).

Conversely, let vspan(S). Then there exist vectors v1, v2, , vm in S and scalars b1, b2, , bm  such that  v=b1v1+b2v2++bmvm. Therefore

0=b1v1+b2v2++bmvm+(1)v.

Note that vvi for i=1, 2, , m because vS. Hence the coefficient of v in this linear combination is nonzero, and so the set {v1, v2, , vm, v} is linearly dependent. Thus S{v} is linearly dependent by Theorem 1.6.

Linearly independent generating sets are investigated in detail in Section 1.6.

Exercises

  1. Label the following statements as true or false.

    1. (a) If S is a linearly dependent set, then each vector in S is a linear combination of other vectors in S.

    2. (b) Any set containing the zero vector is linearly dependent.

    3. (c) The empty set is linearly dependent.

    4. (d) Subsets of linearly dependent sets are linearly dependent.

    5. (e) Subsets of linearly independent sets are linearly independent.

    6. (f) If a1x1+a2x2++anxn=0 and x1, x2, , xn are linearly independent, then all the scalars aj are zero.

  2. 3 Determine whether the following sets are linearly dependent or linearly independent.

    1. (a) {(1324), (2648)} in M2×2(R)

    2. (b) {(1214), (1124)} in M2×2(R)

    3. (c) {x3+2x2,x2+3x+1, x3x2+2x1} in P3(R)

    4. (d) {x3x, 2x2+4,2x3+3x2+2x+6} in P3(R)

    5. (e) {(1,1, 2), (1,2, 1), (1, 1, 4)} in R3

    6. (f) {(1,1, 2), (2, 0, 1), (1, 2,1)} in R3

    7. (g) {(1021), (0111), (1210), (2144)} in M2×2(R)

    8. (h) {(1021), (0111), (1210), (2122)} in M2×2(R)

    9. (i) {x4x3+5x28x+6,x4+x35x2+5x3,       x4+3x23x+5, 2x4+3x3+4x2x+1, x3x+2} in P4(R)

    10. (j) {x4x3+5x28x+6,x4+x35x2+5x3,        x4+3x23x+5, 2x4+x3+4x2+8x} in P4(R)

  3. In M3×2(F), prove that the set

    {(110000), (001100), (000011), (101010), (010101)}

    is linearly dependent.

  4. In Fn, let ej denote the vector whose jth coordinate is 1 and whose other coordinates are 0. Prove that {e1, e2, en} is linearly independent.

  5. Show that the set {1, x, x2, , xn} is linearly independent in Pn(F).

  6. In Mm×n(F), let Eij denote the matrix whose only nonzero entry is 1 in the ith row and jth column. Prove that {Eij: 1im, 1jn} is linearly independent.

  7. Recall from Example 3 in Section 1.3 that the set of diagonal matrices in M2×2(F) is a subspace. Find a linearly independent set that generates this subspace.

  8. Let S={(1, 1, 0), (1, 0, 1), (0, 1, 1)} be a subset of the vector space F3.

    1. (a) Prove that if F=R, then S is linearly independent.

    2. (b) Prove that if F has characteristic two, then S is linearly dependent.

  9. Let u and v be distinct vectors in a vector space V. Show that {u, v} is linearly dependent if and only if u or v is a multiple of the other.

  10. Give an example of three linearly dependent vectors in R3 such that none of the three is a multiple of another.

  11. Let S={u1,u2,,un} be a linearly independent subset of a vector space V over the field Z2. How many vectors are there in span(S)? Justify your answer.

  12. Prove Theorem 1.6 and its corollary.

  13. Let V be a vector space over a field of characteristic not equal to two.

    1. (a) Let u and v be distinct vectors in V. Prove that {u, v} is linearly independent if and only if {u+v,uv} is linearly independent.

    2. (b) Let u, v, and w be distinct vectors in V. Prove that {u, v, w} is linearly independent if and only if {u+v, u+w, v+w} is linearly independent.

  14. Prove that a set S is linearly dependent if and only if S={0} or there exist distinct vectors v, u1, u2, , un in S such that v is a linear combination of u1,u2, ,un.

  15. Let S={u1, u2, , un} be a finite set of vectors. Prove that S is linearly dependent if and only if u1=0 or uk+1 span({u1, u2, , uk}) for some k(1kn).

  16. Prove that a set S of vectors is linearly independent if and only if each finite subset of S is linearly independent.

  17. Let M be a square upper triangular matrix (as defined on page 19 of Section 1.3) with nonzero diagonal entries. Prove that the columns of M are linearly independent.

  18. Let S be a set of nonzero polynomials in P(F) such that no two have the same degree. Prove that S is linearly independent.

  19. Prove that if {A1, A2, , Ak} is a linearly independent subset of Mn×n(F) then { A1t, A2t, , Akt } is also linearly independent.

  20. Let f, g, F(R, R) be the functions defined by f(t)=ert and g(t)=est, where rs. Prove that f and g are linearly independent in F(R, R).

  21. Let S1 and S2 be disjoint linearly independent subsets of V. Prove that S1S2 is linearly dependent if and only if span(S1)span(S2){0}. Visit goo.gl/Fi8Epr for a solution.