Linear Algebra Massoud Malek

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1 CSUEB Linear Algebra Massoud Malek Inner Product and Normed Space In all that follows, the n n identity matrix is denoted by I n, the n n zero matrix by Z n, and the zero vector by θ n An inner product is a generalization of the dot product In a vector space, it is a way to multiply vectors together, with the result of this multiplication being a scalar More precisely, for a real vector space, an inner product, satisfies the following four properties Let u, v, and w be vectors and α be a scalar, then: Linearity: u + v, w = u, w + v, w α v, w = α v, w Symmetry: v, w = w, v Positive-definiteness: v, v 0 and equal if and only if v = 0 A vector space together with an inner product on it is called an inner product space When given a complex vector space, the symmetry property above is usually replaced by Conjugate Symmetry: v, w = w, v, where w, v refers to complex conjugation With this property, the inner product is called a Hermitian inner product and a complex vector space with a Hermitian inner product is called a Hermitian inner product space An inner product space is a vector space with the additional inner product structure An inner product naturally induces an associated norm, u = u, u Thus an inner product space is also a normed vector space Examples of Inner Product Space (a) The Euclidean inner product on IF n, where IF is the set of real or complex number is defined by (u, u 2,, u n, v, v 2,, v n ) = u v + u 2 v u n v n (b) An inner product can be defined on the vector space of smooth real-valued functions on the interval [, ] by f)x), g(x) = f(x) g(x) d x

2 Linear Algebra Inner Product and Normed Space Page 2 (c) An inner product can be defined on the set of real polynomials by Examples of Vector Norms p(x), q(x) = Here are three mostly used vector norms: (a) -norm: v = v i i (b) 2-norm or Euclidean norm: v 2 = v i 2 (c) -norm: v = max x i 0 p(x) q(x) e x d x i Also, note that if is a norm and M is any nonsingular square matrix, then v M v is also a norm The case where M is diagonal is particularly common in practice Note Any Hermitian positive definite matrix H induces an inner product u, v H = u H v; and thus a norm u H = u, u H In MatLab, the -norm, 2-norm, and -norm are invoked by the statements norm (u, ), norm (u, 2), and norm (u, inf), respectively The 2-norm is the default in MatLab The statement norm (u) is interpreted as the 2-norm of u in MatLab Theorem If is a norm induce by an inner product, then u = 0, if and only if u = θ, otherwise u > 0 ; 2 λ u = λ u ; 3 (Cauchy-Schwarz inequality) u, v u v ; 4 (Triangle inequality) u + v u + v, Orthogonality In an inner product vector space V, vectors u and v are orthogonal, if u, v = 0 A subset S of V is orthogonal, if any two distinct vectors of S are orthogonal A vector u is a unit vector, if u = Finally a subset S of V is orthonormal, if S is orthogonal and consists entirely of unit vectors If v θ, then the vector u = v is a unit vector; we say that v was normalized v Note The zero vector θ is orthogonal to every vector in V and θ is the only vector in V that is orthogonal to itself Pythagorean Theorem Suppose u and v are orthogonal vectors in V Then Proof we have u + v 2 = u 2 + v 2 u + v 2 = u + v, u + v = u, u + u, v + v, u + v, v = u, u 2 + v, v 2 as desired

3 Linear Algebra Gram-Schmidt Page 3 The Cauchy-Schwarz inequality states that for all vectors u and v of an inner product space: u, v 2 u, u v, v, where, is the inner product Equivalently, by taking the square root of both sides, and referring to the norms of the vectors, the inequality is written as u, v u v Moreover, the two sides are equal if and only if u and v are linearly dependent Proof Let u, v be arbitrary vectors in a vector space V over IF with an inner product, where IF is the field of real or complex numbers If u, v = 0, the theorem holds trivially If not, then u 0, v 0 For any λ IF, we have 0 u λ v 2 = u λ v, u λ v = u, u λ v λ v, u λ v u, u λ u, v λ v, u + λ λ v, v Choose λ = u, v v, v, then 0 u, u λ u, v λ v, u + λ λ v, v = u, u u, v 2 v, v = u 2 u, v 2 v 2 It follows that u, v 2 u, u v, v or u, v u v Examples of the Cauchy-Schwarz Inequality (a) If u, u 2,, u n, v, v 2,, v n are real numbers, then u v + u 2 v u n v n 2 ( u 2 + u u 2 n) ( v 2 + v v 2 n) (b) If f(x) and g(x) are smooth real-valued functions, then f(x) g(x) d x Triangle Inequality For any vectors u and v, Proof We have 2 ( ) ( f(x) 2 d x u + v u + v g(x) 2 d x u + v 2 = u + v, u + v = u, u + u, v + v, u + v, v = u Re u, v + v 2 u 2 + u, v + v 2 u u v + v 2 = u 2 + v 2 )

4 Linear Algebra Gram-Schmidt Page 4 Gram-Schmidt Process It is often more simpler to work in an orthogonal basis Gram-Schmidt process is a method for obtaining orthonormal vectors from a linearly independent set of vectors in an inner product space, most commonly the Euclidean space IR n S = {v, v 2, v 3,, v k } and generates an orthogonal set S G = {u, u 2, u 3,, u k } or orthonormal basis S N = {e, e 2, e 3,, e k } that spans the same subspace of IR n as the set S v, u We define the projection operator by proj u (v) = u, where v, u denotes the inner u, u product of the vectors v and u This operator projects the vector v orthogonally onto the line spanned by vector u The Gram-Schmidt process then works as follows: u = v, e = u u u 2 = v 2 proj u (v 2 ), e 2 = u 2 u 2 u 3 = v 3 proj u (v 3 ) proj u2 (v 3 ), e 3 = u 3 u 3 u 4 = v 4 proj u (v 4 ) proj u2 (v 4 ) proj u3 (v 4 ), e 4 = u 4 u 4 = = k u k = v k proj uj (v k ), e k = u k u k j= The sequence {u, u 2,, u k } is the required system of orthogonal vectors, and the normalized vectors {e, e 2,, e k } form an orthonormal set The calculation of the sequence u, u 2,, u k is known as Gram-Schmidt orthogonalization, while the calculation of the sequence e, e 2,, e k is known as Gram-Schmidt orthonormalization as the vectors are normalized Example Consider the basis B = v =, v 2 =, v 3 = Then use Gram- Schmidt to find the orthogonal basis B G = { u, u 2, u 3 } and the orthonormal basis B N = { e, e 2, e 3 } associated with B u = v = ; e = u u = 3 u 2 = v 2 v 2, u u, u u = = 2 2 = u 2 = 2 ; e 2 = u u 2 = 2 6 u 3 = v 3 v 3, u u, u u v 3, u 2 u 2, u 2 u 2 = + 2 = 0 ; e 3 = u u 3 = 0 2 Hence B G = u =, u 2 = 2, u 3 = 0 and B N = e =, e 2 = 2, e 3 =

5 Linear Algebra Matrix Norms Page 5 Matrix Norms A matrix norm is an extension of the notion of a vector norm to matrices It is a vector norm on IF m n That is, if A denotes the norm of the matrix A, then, A 0; 2 A = 0 if and only if A = 0; 3 α A = α A for all α IF and A IF m n ; 4 A, B IF m n, A + B A + B Additionally, in the case of square matrices (thus, m = n), some (but not all) matrix norms satisfy the following condition, which is related to the fact that matrices are more than just vectors: 5 AB A B for all matrices A and B in IF m n A matrix norm that satisfies this additional property is called a sub-multiplicative norm Operator Norm If vector norms on IF m and IF n are given (IF is the field of real or complex numbers), then one defines the corresponding operator norm or induced norm or, on the space of m-by-n matrices as the following maxima: A = sup{ Ax : x IF n, x = } { } Ax = sup x : x IF n, x θ The operator norm corresponding to the p-norm for vectors is: Ax p A p = sup x 0 x p Note All operator norms are sub-multiplicative Here are some example matrix norms: m Column norm or -norm: A = max j n i= a ij, which is simply the maximum absolute column sum of the matrix n Row norm or infinity-norm: A = max i m j= a ij, which is simply the maximum absolute row sum of the matrix The 2-norm induced by the euclidean vector norm is A = λ max, where λ max is the largest eigenvalue of the matrix A A Although the Frobenius norm, defined as: k n A F = a ij 2 = trace (A A) i= j=

6 Linear Algebra Matrix Norms Page 6 is not an induced norm but it is a sub-multiplicative norm In MatLab, the Frobenius nom is invoked by the statement norm (A, fro ) The max norm A max = max a ij is neither an induced norm nor a sub-multiplicative norm The largest eigenvalue of a square matrix A in modulus, denoted by ρ (A) and called the spectral radius of A, never exceeds its operator norm: ρ (A) A p Given two normed vector spaces V and W (over the same base field, either the real numbers or the complex numbers), a linear map A : V W is continuous if and only if there exists a real number λ such that for all v V, Condition Number A v λ v The condition number associated with the linear equation A x = b, gives a bound on how inaccurate the solution x will be after approximation Note that this is before the effects of round-off error are taken into account; conditioning is a property of the matrix, not the algorithm or floating point accuracy of the computer used to solve the corresponding system Let x 0 be the solution of the linear equation A x = b and let x be the calculated solution of A x = b, so the error in the solution is e = x x 0, and the relative error in the solution is e = x x 0 x 0 Generally, we can t verify the error or relative error (we don t know x 0 ), so a possible way to check this is by testing the accuracy back in the system A x = b : and relative residual is = residual r = A x 0 A x = b b, A x 0 A x A x = b b b = r b A matrix A is said to be ill-conditioned, if relatively small changes in the input (in the matrix A) can cause large change in the output (the solution of A x = b), ie; the solution is not very accurate if input is rounded Otherwise it is well-conditioned The condition number of an invertible matrix A is defined to be κ (A) = A A This quantity is always greater than or equal to The condition number is defined more precisely to be the maximum ratio of the relative error in x divided by the relative error in b If A is nonsingular, then by knowing the eigenvalues of A A (λ λ 2 λ n > 0), we can compute the condition number using the 2-norm: κ 2 (A) = A 2 A 2 = λ λ n

7 Linear Algebra Matrix Norms Page 7 The smallest nonzero eigenvalue λ n of A A is a measure of how close the matrix is to being singular If λ n is small, then κ 2 (A) is large Thus, the closer the matrix is to being singular, the more ill-conditioned it is Consistent Norms A matrix norm ab on IF m n is called consistent with a vector norm a on IF n and a vector norm b on IF m, if: Ax b A ab u a, for all A IF m n, u IF n All induced norms are consistent by definition Compatible Norms A matrix norm b on IF n n is called compatible with a vector norm a on IF n if: A x a A b x a, for all A IF n n, x IF n All induced norms are compatible by definition Equivalence of Norms Two matrix norms α and β are said to be equivalent, if for all matrices A IF m n, there exist some positive numbers r and s such that Examples of Norm Equivalence r A α A β s A α For matrix A R \ of rank r, the following inequalities hold: A 2 A F r A 2 A F A r A F A max A 2 mn A max A A 2 m A n A A 2 n A m Here, A p refers to the matrix norm induced by the vector p-norm Another useful inequality between matrix norms is: A 2 A A