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Vector Spaces and Linear Maps

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Vector Spaces and Linear Maps Vector Spaces and Linear Maps Garrett Thomas August 14, 2018 1 About This document is part of a series of notes about math and machine learning. You are free to distribute it as you wish. The latest version can be found at http://gwthomas.github.io/notes. Please report any errors to [email protected]. 2 Vector spaces Vector spaces are the basic setting in which linear algebra happens. A vector space over a field F consists of a set V (the elements of which are called vectors) along with an addition operation + : V × V ! V and a scalar multiplication operation F × V ! V satisfying (i) There exists an additive identity, denoted 0, in V such that v + 0 = v for all v 2 V (ii) For each v 2 V , there exists an additive inverse, denoted −v, such that v + (−v) = 0 (iii) There exists a multiplicative identity, denoted 1, in F such that 1v = v for all v 2 V (iv) Commutativity: u + v = v + u for all u; v 2 V (v) Associativity: (u + v) + w = u + (v + w) and α(βv) = (αβ)v for all u; v; w 2 V and α; β 2 F (vi) Distributivity: α(u + v) = αu + αv and (α + β)v = αv + βv for all u; v 2 V and α; β 2 F We will assume F = R or F = C as these are the most common cases by far, although in general it could be any field. To justify the notations 0 for the additive identity and −v for the additive inverse of v we must demonstrate their uniqueness. Proof. If w; w~ 2 V satisfy v + w = v and v +w ~ = v for all v 2 V , then w = w +w ~ =w ~ + w =w ~ This proves the uniqueness of the additive identity. Similarly, if w; w~ 2 V satisfy v + w = 0 and v +w ~ = 0, then w = w + 0 = w + v +w ~ = 0 +w ~ =w ~ thus showing the uniqueness of the additive inverse. 1 We have 0v = 0 for every v 2 V and α0 = 0 for every α 2 F. Proof. If v 2 V , then v = 1v = (1 + 0)v = 1v + 0v = v + 0v which implies 0v = 0. If α 2 F, then αv = α(v + 0) = αv + α0 which implies α0 = 0. Moreover, (−1)v = −v for every v 2 V , since v + (−1)v = (1 + (−1))v = 0v = 0 and we have shown that additive inverses are unique. 2.1 Subspaces Vector spaces can contain other vector spaces. If V is a vector space, then S ⊆ V is said to be a subspace of V if (i)0 2 S (ii) S is closed under addition: u; v 2 S implies u + v 2 S (iii) S is closed under scalar multiplication: v 2 S; α 2 F implies αv 2 S Note that V is always a subspace of V , as is the trivial vector space which contains only 0. Proposition 1. Suppose U and W are subspaces of some vector space. Then U \ W is a subspace of U and a subspace of W . Proof. We only show that U \ W is a subspace of U; the same result follows for W since U \ W = W \ U. (i) Since 0 2 U and 0 2 W by virtue of their being subspaces, we have 0 2 U \ W . (ii) If x; y 2 U \ W , then x; y 2 U so x + y 2 U, and x; y 2 W so x + y 2 W ; hence x + y 2 U \ W . (iii) If v 2 U \ W and α 2 F, then v 2 U so αv 2 U, and v 2 W so αv 2 W ; hence αv 2 U \ W . Thus U \ W is a subspace of U. 2.1.1 Sums of subspaces If U and W are subspaces of V , then their sum is defined as U + W , fu + w : u 2 U; w 2 W g Proposition 2. Let U and W be subspaces of V . Then U + W is a subspace of V . Proof. (i) Since 0 2 U and 0 2 V , 0 = 0 + 0 2 U + W . 2 (ii) If v1; v2 2 U + W , then there exist u1; u2 2 U and w1; w2 2 W such that v1 = u1 + w1 and v2 = u2 + w2, so by the closure of U and W under addition we have v1 + v2 = u1 + w1 + u2 + w2 = u1 + u2 + w1 + w2 2 U + W | {z } | {z } 2U 2W (iii) If v 2 U + W; α 2 F , then there exists u 2 U and w 2 W such that v = u + w, so by the closure of U and W under scalar multiplication we have αv = α(u + w) = αu + αw 2 U + W |{z} |{z} 2U 2W Thus U + W is a subspace of V . If U \ W = f0g, the sum is said to be a direct sum and written U ⊕ W . Proposition 3. Suppose U and W are subspaces of some vector space. The following are equivalent: (i) The sum U + W is direct, i.e. U \ W = f0g. (ii) The only way to write 0 as a sum u + w where u 2 U; w 2 W is to take u = w = 0. (iii) Every vector in U + W can be written uniquely as u + w for some u 2 U and w 2 W . Proof. (i) =) (ii): Assume (i), and suppose 0 = u + w where u 2 U; w 2 W . Then u = −w, so u 2 W as well, and thus u 2 U \ W . By assumption this implies u = 0, so w = 0 also. (ii) =) (iii): Assume (ii), and suppose v 2 U + W can be written both as v = u + w and v =u ~ +w ~ where u; u~ 2 U and w; w~ 2 W . Then 0 = v − v = u + w − (~u +w ~) = u − u~ + w − w~ | {z } | {z } 2U 2W so by assumption we must have u − u~ = w − w~ = 0, i.e. u =u ~ and w =w ~. (iii) =) (i): Assume (iii), and suppose v 2 U \W , so that v 2 U and v 2 W , nothing that −v 2 W also. Now 0 = v + (−v) and 0 = 0 + 0 both write 0 in the form u + w where u 2 U; w 2 W , so by the assumed uniqueness of this decomposition we conclude that v = 0. Thus U \ W ⊆ f0g. It is also clear that f0g ⊆ U \ W since U and W are both subspaces, so U \ W = f0g. 2.2 Span A linear combination of vectors v1; : : : ; vn 2 V is an expression of the form α1v1 + ··· + αnvn where α1; : : : ; αn 2 F. The span of a set of vectors is the set of all possible linear combinations of these: spanfv1; : : : ; vng , fα1v1 + ··· + αnvn : α1; : : : ; αn 2 Fg We also define the special case spanfg = f0g. Spans of sets of vectors form subspaces. Proposition 4. If v1; : : : ; vn 2 V , then spanfv1; : : : ; vng is a subspace of V . 3 Proof. Let S = spanfv1; : : : ; vng. If n = 0, we have S = spanfg = f0g, which is a subspace of V , so assume hereafter that n > 0. (i) Since fv1; : : : ; vng is non-empty (as n ≥ 1), we have 0 = 0v1 + ··· + 0vn 2 S. (ii) If x; y 2 S, we can write x = α1v1 + ··· + αnvn and y = β1v1 + ··· + βnvn for some α1; : : : ; αn; β1; : : : ; βn 2 F. Then x + y = α1v1 + ··· + αnvn + β1v1 + ··· + βnvn = (α1 + β1)v1 + ··· + (αn + βn)vn 2 S (iii) If x 2 S; β 2 F, we can write x = α1v1 + ··· + αnvn for some α1; : : : ; αn 2 F. Then βx = β(α1v1 + ··· + αnvn) = (βα1)v1 + ··· + (βαn)vn 2 S Thus S is a subspace of V . Adding a vector which lies in the span of a set of vectors to that set does not expand the span in any way. Proposition 5. If w 2 spanfv1; : : : ; vng, then spanfv1; : : : ; vn; wg = spanfv1; : : : ; vng. Proof. Suppose w 2 spanfv1; : : : ; vng. It is clear that spanfv1; : : : ; vng ⊆ spanfv1; : : : ; vn; wg: if u 2 spanfv1; : : : ; vng then it can be written u = α1v1+···+αnvn, but then u = α1v1+···+αnvn+0w, so u 2 spanfv1; : : : ; vn; wg. To prove the other direction, write w = β1v1 + ··· + βnvn. Then for any u 2 spanfv1; : : : ; vm; wg, there exist α1; : : : ; αn; αn+1 such that u = α1v1 + ··· + αnvn + αn+1w, and u = α1v1 + ··· + αnvn + αn+1(β1v1 + ··· + βnvn) = (α1 + αn+1β1)v1 + ··· + (αn + αn+1βn)vn so u 2 spanfv1; : : : ; vmg. Hence spanfv1; : : : ; vn; wg ⊆ spanfv1; : : : ; vng and the claim follows. 2.3 Linear independence A set of vectors v1; : : : ; vn 2 V is said to be linearly independent if α1v1 + ··· + αnvn = 0 implies α1 = ··· = αn = 0: Otherwise, there exist α1; : : : ; αn 2 F, not all zero, such that α1v1 + ··· + αnvn = 0, and v1; : : : ; vn are said to be linearly dependent. Note that in the case of linear dependence, (at least) one vector can be written as a linear combination of the other vectors in the set: if αj 6= 0, then n 1 X vj = − αkvk αj k6=j This implies that vj 2 spanfv1; : : : ; vj−1; vj+1; : : : ; vng. 4 2.4 Basis If a set of vectors is linearly independent and its span is the whole of V , those vectors are said to be a basis for V . One of the most important properties of bases is that they provide unique representations for every vector in the space they span. Proposition 6. A set of vectors v1; : : : ; vn 2 V is a basis for V if and only if every vector in V can be written uniquely as a linear combination of v1; : : : ; vn.
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