Linear Transformations and Subspaces: Properties and Equivalence - Prof. Dmytro Kaliuzhnyi, Assignments of Linear Algebra

Solutions to two problems related to linear transformations and subspaces in a vector space. The first problem deals with determining which subsets of the vector space c[-1, 1] are subspaces based on given conditions. The second problem discusses the equivalence of certain properties of a linear transformation t, including left invertibility, having an empty kernel, and being one-to-one.

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Pre 2010

Uploaded on 08/19/2009

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WA 3: Solutions
1. Which of the following subsets of the vector space C[1,1] of continuous
functions on the segment [1,1] are subspaces (justify your answers)?
(a) The set Xof functions fwhich satisfy f(1) + f(1) = 1.
(b) The set Yof functions fwhich satisfy
f(t) = f(t) for every t [1,1].
(Such functions are called even.)
Solution. (a) The zero function f(t)0 satisfies f(1) + f(1) = 0 + 0 = 0 6= 1,
thus this function doesn’t belong to the set X. Therefore, Xis not a subspace of
the vector space C[1,1].
(b) The zero function f(t)0 satisfies f(t) = f(t) = 0 for every t[1,1],
thus this function belongs to the set Y.
For any two functions fand gfrom Y,
(f+g)(t) = f(t) + g(t) = f(t) + g(t) = (f+g)(t) for every t[1,1].
Thus f+gY.
For an arbitrary fYand cRone has
(cf)(t) = cf(t) = cf(t) = (cf)(t) for every t[1,1].
Thus cf Y.
Finally, we conclude that Yis a subspace of the vector space C[1,1].
2. Let Tbe a linear transformation mapping a vector space Vonto a vector
space W. Prove that the following statements are equivalent:
(a) Tis left invertible, i.e., there exists a linear transformation Swhich satisfies
S(T(x)) = xfor every x V;
(b) ker T={0};
(c) Tis a one-to-one transformation.
Solution. (a)(b). If ker T6={0}then there exists a vector x6= 0 such that
T(x) = 0, and we have
x=S(T(x)) = S(0) = 0,
which contradicts to the assumption that x6= 0. Therefore, ker T={0}.
(b)(c). If for a vector yWthere exist two vectors xand x0in Vsuch that
T(x) = T(x0) = ythen
T(xx0) = T(x)T(x0) = yy=0,
i.e., xx0ker T. Since ker T={0}by the assumption, we get xx0=0, i.e.,
x=x0. This means that for an arbitrary vector yWthe equation T(x) = yhas
at most one solution, i.e., Tis a one-to-one transformation.
(c)(a). Since Tis one-to-one and onto, for an arbitrary yWthe equation
T(x) = yhas exactly one solution xV. Denote this unique solution by S(y).
So, the transformation S:WVis well defined, and S(T(x)) = x. We only have
to show now that this transformation Sis linear.
For arbitrary vectors yand y0from W, we have S(y) = xif and only if T(x) = y,
and S(y0) = x0if and only if T(x0) = y0. Since the transformation Tis linear, we
have
T(x+x0) = T(x) + T(x0) = y+y0.
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WA 3: Solutions

  1. Which of the following subsets of the vector space C[− 1 , 1] of continuous functions on the segment [− 1 , 1] are subspaces (justify your answers)? (a) The set X of functions f which satisfy f (−1) + f (1) = 1. (b) The set Y of functions f which satisfy f (−t) = f (t) for every t ∈ [− 1 , 1].

(Such functions are called even.)

Solution. (a) The zero function f (t) ≡ 0 satisfies f (−1) + f (1) = 0 + 0 = 0 6 = 1, thus this function doesn’t belong to the set X. Therefore, X is not a subspace of the vector space C[− 1 , 1]. (b) The zero function f (t) ≡ 0 satisfies f (−t) = f (t) = 0 for every t ∈ [− 1 , 1], thus this function belongs to the set Y. For any two functions f and g from Y , (f + g)(−t) = f (−t) + g(−t) = f (t) + g(t) = (f + g)(t) for every t ∈ [− 1 , 1].

Thus f + g ∈ Y. For an arbitrary f ∈ Y and c ∈ R one has (cf )(−t) = cf (−t) = cf (t) = (cf )(t) for every t ∈ [− 1 , 1].

Thus cf ∈ Y. Finally, we conclude that Y is a subspace of the vector space C[− 1 , 1].

  1. Let T be a linear transformation mapping a vector space V onto a vector space W. Prove that the following statements are equivalent: (a) T is left invertible, i.e., there exists a linear transformation S which satisfies S(T (x)) = x for every x ∈ V; (b) ker T = { 0 }; (c) T is a one-to-one transformation. Solution. (a)⇒(b). If ker T 6 = { 0 } then there exists a vector x 6 = 0 such that T (x) = 0 , and we have x = S(T (x)) = S( 0 ) = 0 ,

which contradicts to the assumption that x 6 = 0. Therefore, ker T = { 0 }. (b)⇒(c). If for a vector y ∈ W there exist two vectors x and x′^ in V such that T (x) = T (x′) = y then

T (x − x′) = T (x) − T (x′) = y − y = 0 ,

i.e., x − x′^ ∈ ker T. Since ker T = { 0 } by the assumption, we get x − x′^ = 0 , i.e., x = x′. This means that for an arbitrary vector y ∈ W the equation T (x) = y has at most one solution, i.e., T is a one-to-one transformation. (c)⇒(a). Since T is one-to-one and onto, for an arbitrary y ∈ W the equation T (x) = y has exactly one solution x ∈ V. Denote this unique solution by S(y). So, the transformation S : W → V is well defined, and S(T (x)) = x. We only have to show now that this transformation S is linear. For arbitrary vectors y and y′^ from W , we have S(y) = x if and only if T (x) = y, and S(y′) = x′^ if and only if T (x′) = y′. Since the transformation T is linear, we have T (x + x′) = T (x) + T (x′) = y + y′. 1

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Therefore, S(y + y′) = x + x′^ = S(y) + S(y′). For arbitrary vector y ∈ W and scalar c ∈ R from W , we have S(y) = x if and only if T (x) = y. Since the transformation T is linear, we have

T (cx) = cT (x) = cy.

Therefore, S(cy) = cx = cS(y). We have found the transformation S with all the required properties, i.e., (a) is true. Since (a)⇒(b)⇒(c)⇒(a), all the statements (a), (b), and (c) are equivalent.