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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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WA 3: Solutions
(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].
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.