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Vector Spaces Vector Spaces Math 240 Definition Properties Set notation Subspaces Vector Spaces Math 240 | Calculus III Summer 2013, Session II Wednesday, July 17, 2013 Vector Spaces Agenda Math 240 Definition Properties Set notation Subspaces 1. Definition 2. Properties of vector spaces 3. Set notation 4. Subspaces Vector Spaces Motivation Math 240 Definition Properties Set notation Subspaces We know a lot about Euclidean space. There is a larger class of n objects that behave like vectors in R . What do all of these objects have in common? Vector addition a way of combining two vectors, u and v, into the single vector u + v Scalar multiplication a way of combining a scalar, k, with a vector, v, to end up with the vector kv A vector space is any set of objects with a notion of addition n and scalar multiplication that behave like vectors in R . Vector Spaces Examples of vector spaces Math 240 Definition Real vector spaces Properties n Set notation I R (the archetype of a vector space) Subspaces I R | the set of real numbers I Mm×n(R) | the set of all m × n matrices with real entries for fixed m and n. If m = n, just write Mn(R). I Pn | the set of polynomials with real coefficients of degree at most n I P | the set of all polynomials with real coefficients k I C (I) | the set of all real-valued functions on the interval I having k continuous derivatives Complex vector spaces n I C, C I Mm×n(C) Vector Spaces Definition Math 240 Definition Properties Definition Set notation A vector space consists of a set of scalars, a nonempty set, V , Subspaces whose elements are called vectors, and the operations of vector addition and scalar multiplication satisfying 1. Closure under addition: For each pair of vectors u and v, the sum u + v is an element of V . 2. Closure under scalar multiplication: For each vector v and scalar k, the scalar multiple kv is an element of V . 3. Commutativity of addition: For all u; v 2 V , we have u + v = v + u. 4. Associativity of addition: For all u; v; w 2 V , we have (u + v) + w = u + (v + w). 5. Existence of a zero vector: There is a vector 0 2 V satisfying v + 0 = v for all v 2 V . Vector Spaces Definition Math 240 Definition Properties Definition Set notation A vector space consists of a set of scalars, a nonempty set, V , Subspaces whose elements are called vectors, and the operations of vector addition and scalar multiplication satisfying 6. Existence of additive inverses: For each v 2 V , there is a vector −v 2 V such that v + (−v) = 0. 7. Unit property: For all vectors v, we have 1v = v. 8. Associativity of scalar multiplication: For all vectors v and scalars r; s, we have (rs)v = r(sv). 9. Distributive property of scalar multiplication over vector addition: For all vectors u and v and scalars r, we have r(u + v) = ru + rv. 10. Disributive property of scalar multiplication over scalar addition: For all vectors v and scalars r and s, we have (r + s)v = rv + sv. Vector Spaces Example Math 240 Definition Let's verify that M2(R) is a vector space. Properties 1. From the definition of matrix addition, we know that the Set notation sum of two 2 × 2 matrices is also a 2 × 2 matrix. Subspaces 2. From the definition of scalar-matrix multiplication, we know that multiplying a 2 × 2 matrix by a scalar results in a 2 × 2 matrix. 3. Given two 2 × 2 matrices a a b b A = 1 2 and B = 1 2 ; a3 a4 b3 b4 their sum is a + b a + b A + B = 1 1 2 2 a3 + b3 a4 + b4 b + a b + a = 1 1 2 2 = B + A: b3 + a3 b4 + a4 Vector Spaces Example Math 240 Definition Let's verify that M2(R) is a vector space. Properties 4. Given three 2 × 2 matrices Set notation Subspaces a a b b c c A = 1 2 ;B = 1 2 ;C = 1 2 ; a3 a4 b3 b4 c3 c4 we have (a + b ) + c (a + b ) + c (A + B) + C = 1 1 1 2 2 2 (a3 + b3) + c3 (a4 + b4) + c4 a + (b + c ) a + (b + c ) = 1 1 1 2 2 2 a3 + (b3 + c3) a4 + (b4 + c4) = A + (B + C): 0 0 5. If A 2 M ( ) then A + = A, so the zero vector in 2 R 0 0 0 0 M ( ) is 0 = . 2 R 0 0 Vector Spaces Example Math 240 Definition Let's verify that M2(R) is a vector space. Properties a b −a −b Set notation 6. The additive inverse of A = is −A = c d −c −d Subspaces because a + (−a) b + (−b) 0 0 A + (−A) = = = 0: c + (−c) d + (−d) 0 0 7. If A is any matrix, then obviously 1A = A. a b 8. Given a matrix A = and scalars r and s, we have c d (rs)a (rs)b r(sa) r(sb) (rs)A = = (rs)c (rs)d r(sc) r(sd) sa sb = r = r(sA): sc sd Vector Spaces Example Math 240 Definition Let's verify that M2(R) is a vector space. Properties a1 a2 b1 b2 Set notation 9. Given matrices A = and B = and a a a b b Subspaces 3 4 3 4 scalar r, we have r(a + b ) r(a + b ) r(A + B) = 1 1 2 2 r(a3 + b3) r(a4 + b4) ra + rb ra + rb = 1 1 2 2 = rA + rB: ra3 + rb3 ra4 + rb4 a b 10. Given a matrix A = and scalars r and s, we have c d (r + s)a (r + s)b (r + s)A = (r + s)c (r + s)d ra + sa rb + sb = = rA + sA: rc + sc rd + sd Vector Spaces Additional properties of vector spaces Math 240 Definition Properties Set notation Subspaces The following properties are consequences of the vector space axioms. I The zero vector is unique. I 0u = 0 for all u 2 V . I k0 = 0 for all scalar k. I The additive inverse of a vector is unique. I For all u 2 V , its additive inverse is given by −u = (−1)u. I If k is a scalar and u 2 V such that ku = 0 then either k = 0 or u = 0. Vector Spaces Aside: set notation Math 240 Definition Definition Properties Let V be a set. We write the subset of V satisfying some Set notation conditions as Subspaces S = fv 2 V : conditions on vg : Examples 1. The plane −3x + 2y + z = 4 can be written 3 (x; y; z) 2 R : −3x + 2y + z = 4 : 2. The line perpendicular to this plane passing through the point (1; 0; 0) can be written 3 x 2 R : x = (1 − 3r; 2r; r); r 2 R or 3 (1 − 3r; 2r; r) 2 R : r 2 R : Vector Spaces Practice problem Math 240 Definition Properties Set notation If A is an m × n matrix, verify that Subspaces n V = fx 2 R : Ax = 0g is a vector space. n n R is a vector space. V is a subset of R and also a vector space. One vector space inside another?!? What about n W = fx 2 R : Ax = bg where b 6= 0? Vector Spaces Definition Math 240 Definition Definition Properties Suppose V is a vector space and S is a nonempty subset of V . Set notation Subspaces We say that S is a subspace of V if S is a vector space under the same addition and scalar multiplication as V . Examples 1. Any vector space has two improper subspaces: f0g and the vector space itself. Other subspaces are called proper. 2. The solution set of a homogeneous linear system is a n subspace of R . This includes all lines, planes, and hyperplanes through the origin. 3. The set of polynomials in P2 with no linear term forms a subspace of P2. In turn, P2 is a subspace of P . 4. Ck(I) is a subspace of C`(I) for all intervals I and all k ≥ `. Vector Spaces Criteria for subspaces Math 240 Definition Properties Set notation Subspaces Checking all 10 axioms for a subspace is a lot of work. Fortunately, it's not necessary. Theorem If V is a vector space and S is a nonempty subset of V then S is a subspace of V if and only if S is closed under the addition and scalar multiplication in V . Remark Don't forget the \nonempty." It's often quicker and easier to just check that 0 2 S. Vector Spaces Example Math 240 Definition Let S denote the set of real symmetric n × n matrices. Let's Properties check that S is a subspace of Mn( ). Set notation R Subspaces First, write S as T S = A 2 Mn(R): A = A : Now, check three things: 1. 0 2 S: Obvious. 2. If A; B 2 S then A + B 2 S: (A + B)T = AT + BT = A + B 3. If A 2 S and k is a scalar then kA 2 S: (kA)T = kAT = kA It's a subspace! Vector Spaces The null space of a matrix Math 240 Definition Properties Definition Set notation If A is an m × n matrix, the solution space of the homogeneous Subspaces linear system Ax = 0 is called the null space of A.
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