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D Matrix Calculus D–1 Appendix D: MATRIX CALCULUS D–2 In this Appendix we collect some useful formulas of matrix calculus that often appear in finite element derivations. §D.1 THE DERIVATIVES OF VECTOR FUNCTIONS Let x and y be vectors of orders n and m respectively: x1 y1 x2 y2 x = . , y = . ,(D.1) . xn ym where each component yi may be a function of all the xj , a fact represented by saying that y is a function of x,or y = y(x). (D.2) If n = 1, x reduces to a scalar, which we call x.Ifm = 1, y reduces to a scalar, which we call y. Various applications are studied in the following subsections. §D.1.1 Derivative of Vector with Respect to Vector The derivative of the vector y with respect to vector x is the n × m matrix ∂y1 ∂y2 ∂ym ∂ ∂ ··· ∂ x1 x1 x1 ∂ ∂ ∂ ∂ y1 y2 ··· ym y def ∂x ∂x ∂x = 2 2 2 (D.3) ∂x . . .. ∂ ∂ ∂ y1 y2 ··· ym ∂xn ∂xn ∂xn §D.1.2 Derivative of a Scalar with Respect to Vector If y is a scalar, ∂y ∂x1 ∂ ∂ y y def ∂ = x2 .(D.4) ∂x . . ∂y ∂xn §D.1.3 Derivative of Vector with Respect to Scalar If x is a scalar, ∂y def ∂ ∂ ∂ = y1 y2 ... ym (D.5) ∂x ∂x ∂x ∂x D–2 D–3 §D.1 THE DERIVATIVES OF VECTOR FUNCTIONS REMARK D.1 Many authors, notably in statistics and economics, define the derivatives as the transposes of those given above.1 This has the advantage of better agreement of matrix products with composition schemes such as the chain rule. Evidently the notation is not yet stable. EXAMPLE D.1 Given x1 y1 y = , x = x2 (D.6) y2 x3 and 2 y1 = x − x 1 2 (D.7) = 2 + y2 x3 3x2 the partial derivative matrix ∂y/∂x is computed as follows: ∂ ∂ y1 y2 ∂x1 ∂x1 ∂y ∂ ∂ 2x1 0 = y1 y2 = − ( . ) ∂ ∂ 13 D 8 ∂x x2 x2 02x3 ∂y1 ∂y2 ∂x3 ∂x3 §D.1.4 Jacobian of a Variable Transformation In multivariate analysis, if x and y are of the same order, the determinant of the square matrix ∂x/∂y, that is ∂x J = (D.9) ∂y is called the Jacobian of the transformation determined by y = y(x). The inverse determinant is ∂ −1 y J = .(D.10) ∂x 1 One authors puts is this way: “When one does matrix calculus, one quickly finds that there are two kinds of people in this world: those who think the gradient is a row vector, and those who think it is a column vector.” D–3 Appendix D: MATRIX CALCULUS D–4 EXAMPLE D.2 The transformation from spherical to Cartesian coordinates is defined by x = r sin θ cos ψ, y = r sin θ sin ψ, z = r cos θ(D.11) where r > 0, 0 <θ<πand 0 ≤ ψ<2π. To obtain the Jacobian of the transformation, let x ≡ x , y ≡ x , z ≡ x 1 2 3 (D.12) r ≡ y1,θ≡ y2,ψ≡ y3 Then ∂ sin y2 cos y3 sin y2 sin y3 cos y2 = x = − J y1 cos y2 cos y3 y1 cos y2 sin y3 y1 sin y2 ∂y (D.13) −y1 sin y2 sin y3 y1 sin y2 cos y3 0 = 2 = 2 θ. y1 sin y2 r sin The foregoing definitions can be used to obtain derivatives to many frequently used expressions, including quadratic and bilinear forms. EXAMPLE D.3 Consider the quadratic form y = xT Ax (D.14) where A is a square matrix of order n. Using the definition (D.3) one obtains ∂y = Ax + AT x (D.15) ∂x and if A is symmetric, ∂y = 2Ax.(D.16) ∂x We can of course continue the differentiation process: ∂2 y ∂ ∂y = = A + AT ,(D.17) ∂x2 ∂x ∂x and if A is symmetric, ∂2 y = 2A.(D.18) ∂x2 The following table collects several useful vector derivative formulas. ∂y y ∂x Ax AT xT AA xT x 2x xT Ax Ax + AT x D–4 D–5 §D.2 THE CHAIN RULE FOR VECTOR FUNCTIONS §D.2 THE CHAIN RULE FOR VECTOR FUNCTIONS Let x1 y1 z1 x2 y2 z2 x = . , y = . and z = . (D.19) . xn yr zm where z is a function of y, which is in turn a function of x. Using the definition (D.2), we can write ∂ ∂ ∂ z1 z1 ... z1 ∂x1 ∂x2 ∂xn ∂ ∂ ∂ T z2 z2 ... z2 ∂z ∂x ∂x ∂x = 1 2 n ( . ) ∂ . D 20 x . ∂ ∂ ∂ zm zm ... zm ∂x1 ∂x2 ∂xn Each entry of this matrix may be expanded as r ∂zi ∂zi ∂yq i = 1, 2,...,m = (D.21) ∂ ∂ ∂ j = 1, 2,...,n. xj q=1 yq xj Then ∂ ∂y ∂ ∂y ∂ ∂y z1 q z1 q ... z2 q ∂ ∂ ∂ ∂ ∂ yq x1 yq x2 yq xn ∂ ∂y ∂ ∂y ∂ ∂y T z2 q z2 q ... z2 q ∂z ∂y ∂x ∂y ∂x ∂y ∂x = q 1 q 2 q n ∂ . x . ∂ ∂y ∂ ∂y ∂ ∂y zm q zm q ... zm q ∂yq ∂x1 ∂yq ∂x2 ∂yq ∂xn ∂ ∂ ∂ ∂ ∂ ∂ z1 z1 ... z1 y1 y1 ... y1 ∂y1 ∂y2 ∂yr ∂x1 ∂x2 ∂xn ∂ ∂ ∂ ∂ ∂ ∂ z2 z2 ... z2 y2 y2 ... y2 ∂ ∂ ∂ ∂ ∂ ∂ y1 y2 yr x1 x2 xn = . . . . ∂ ∂ ∂ ∂ ∂ ∂ zm zm ... zm yr yr ... yr ∂y ∂y ∂y ∂x ∂x ∂x 1 2 r 1 2 n ∂z T ∂y T ∂y ∂z T = = .(D.22) ∂y ∂x ∂x ∂y On transposing both sides, we finally obtain ∂z ∂y ∂z = ,(D.23) ∂x ∂x ∂y which is the chain rule for vectors. If all vectors reduce to scalars, ∂z ∂y ∂z ∂z ∂y = = ,(D.24) ∂x ∂x ∂y ∂y ∂x D–5 Appendix D: MATRIX CALCULUS D–6 which is the conventional chain rule of calculus. Note, however, that when we are dealing with vectors, the chain of matrices builds “toward the left.” For example, if w is a function of z, which is a function of y, which is a function of x, ∂w ∂y ∂z ∂w = .(D.25) ∂x ∂x ∂y ∂z On the other hand, in the ordinary chain rule one can indistictly build the product to the right or to the left because scalar multiplication is commutative. §D.3 THE DERIVATIVE OF SCALAR FUNCTIONS OF A MATRIX Let X = (xij) be a matrix of order (m × n) and let y = f (X), (D.26) be a scalar function of X. The derivative of y with respect to X, denoted by ∂y ,(D.27) ∂X is defined as the following matrix of order (m × n): ∂y ∂y ... ∂y ∂x ∂x ∂x 11 12 1n ∂y ∂y ... ∂y ∂y ∂ ∂ ∂ ∂y ∂y G = = x21 x22 x2n = = E ,(D.28) ∂ . ∂ ij ∂ X . xij i, j xij ∂y ∂y ... ∂y ∂xm1 ∂xm2 ∂xmn where Eij denotes the elementary matrix* of order (m × n). This matrix G is also known as a gradient matrix. EXAMPLE D.4 Find the gradient matrix if y is the trace of a square matrix X of order n, that is n y = tr(X) = xii.(D.29) i=1 Obviously all non-diagonal partials vanish whereas the diagonal partials equal one, thus ∂y G = = I,(D.30) ∂X where I denotes the identity matrix of order n. * The elementary matrix Eij of order m × n has all zero entries except for the (i, j) entry, which is one. D–6 D–7 §D.4 THE MATRIX DIFFERENTIAL §D.3.1 Functions of a Matrix Determinant An important family of derivatives with respect to a matrix involves functions of the determinant of a matrix, for example y =|X| or y =|AX|. Suppose that we have a matrix Y = [yij] whose components are functions of a matrix X = [xrs], that is yij = fij(xrs), and set out to build the matrix ∂|Y| .(D.31) ∂X Using the chain rule we can write ∂| | ∂| | ∂ Y = Y yij .(. ) ∂ Yij ∂ ∂ D 32 xrs i j yij xrs But |Y|= yijYij,(D.33) j where Yij is the cofactor of the element yij in |Y|. Since the cofactors Yi1, Yi2, ... are independent of the element yij,wehave ∂|Y| = Yij.(D.34) ∂yij It follows that ∂| | ∂ Y = yij .(. ) ∂ Yij ∂ D 35 xrs i j xrs There is an alternative form of this result which is ocassionally useful. Define ∂yij aij = Yij, A = [aij], bij = , B = [bij].(D.36) ∂xrs Then it can be shown that ∂|Y| = tr(ABT ) = tr(BT A). (D.37) ∂xrs EXAMPLE D.5 If X is a nonsingular square matrix and Z =|X|X−1 its cofactor matrix, ∂|X| G = = ZT .(D.38) ∂X If X is also symmetric, ∂|X| G = = 2ZT − diag(ZT ). (D.39) ∂X D–7 Appendix D: MATRIX CALCULUS D–8 §D.4 THE MATRIX DIFFERENTIAL For a scalar function f (x), where x is an n-vector, the ordinary differential of multivariate calculus is defined as n ∂ = f .(. ) df ∂ dxi D 40 i=1 xi In harmony with this formula, we define the differential of an m × n matrix X = [xij]tobe dx11 dx12 ... dx1n dx21 dx22 ... dx2n dX def= . .(D.41) . dxm1 dxm2 ... dxmn This definition complies with the multiplicative and associative rules d(αX) = α dX, d(X + Y) = dX + dY.(D.42) If X and Y are product-conforming matrices, it can be verified that the differential of their product is d(XY) = (dX)Y + X(dY). (D.43) which is an extension of the well known rule d(xy) = ydx+ xdyfor scalar functions. EXAMPLE D.6 −1 If X = [xij] is a square nonsingular matrix of order n, and denote Z =|X|X . Find the differential of the determinant of X: ∂| | X −1 T T d|X|= dxij = Xij dxij = tr(|X|X ) dX) = tr(Z dX), (D.44) ∂xij i, j i, j where Xij denotes the cofactor of xij in X.
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  • Some Important Properties for Matrix Calculus

    Some Important Properties for Matrix Calculus

    Some Important Properties for Matrix Calculus Dawen Liang Carnegie Mellon University [email protected] 1 Introduction Matrix calculation plays an essential role in many machine learning algorithms, among which ma- trix calculus is the most commonly used tool. In this note, based on the properties from the dif- ferential calculus, we show that they are all adaptable to the matrix calculus1. And in the end, an example on least-square linear regression is presented. 2 Notation A matrix is represented as a bold upper letter, e.g. X, where Xm;n indicates the numbers of rows and columns are m and n, respectively. A vector is represented as a bold lower letter, e.g. x, where it is a n × 1 column vector in this note. An important concept for a n × n matrix An;n is the trace Tr(A), which is defined as the sum of the diagonal: n X Tr(A) = Aii (1) i=1 where Aii index the element at the ith row and ith column. 3 Properties The derivative of a matrix is usually referred as the gradient, denoted as r. Consider a function m×n p×q f : R ! R , the gradient for f(A) w.r.t. Am;n is: 0 1 @f @f ··· @f @A11 @A12 @A1n B @f @f ··· @f C @f(A) B @A21 @A22 @A2n C rAf(A) = = B . C @A B . .. C @ . A @f @f ··· @f @Am1 @Am2 @Amn This definition is very similar to the differential derivative, thus a few simple properties hold (the matrix A below is square matrix and has the same dimension with the vectors): T T rxb Ax = b A (2) 1Some of the detailed derivations which are omitted in this note can be found at http://www.cs.berkeley.
  • An Exterior Algebraic Derivation of the Euler–Lagrange Equations from the Principle of Stationary Action

    An Exterior Algebraic Derivation of the Euler–Lagrange Equations from the Principle of Stationary Action

    mathematics Article An Exterior Algebraic Derivation of the Euler–Lagrange Equations from the Principle of Stationary Action Ivano Colombaro * , Josep Font-Segura and Alfonso Martinez Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08018 Barcelona, Spain; [email protected] (J.F.-S.); [email protected] (A.M.) * Correspondence: [email protected] Abstract: In this paper, we review two related aspects of field theory: the modeling of the fields by means of exterior algebra and calculus, and the derivation of the field dynamics, i.e., the Euler– Lagrange equations, by means of the stationary action principle. In contrast to the usual tensorial derivation of these equations for field theories, that gives separate equations for the field components, two related coordinate-free forms of the Euler–Lagrange equations are derived. These alternative forms of the equations, reminiscent of the formulae of vector calculus, are expressed in terms of vector derivatives of the Lagrangian density. The first form is valid for a generic Lagrangian density that only depends on the first-order derivatives of the field. The second form, expressed in exterior algebra notation, is specific to the case when the Lagrangian density is a function of the exterior and interior derivatives of the multivector field. As an application, a Lagrangian density for generalized electromagnetic multivector fields of arbitrary grade is postulated and shown to have, by taking the vector derivative of the Lagrangian density, the generalized Maxwell equations as Euler–Lagrange equations. Citation: Colombaro, I.; Font-Segura, J.; Keywords: Euler–Lagrange equations; exterior algebra; exterior calculus; tensor calculus; action Martinez, A.