Higher derivatives as multilinear maps Suppose U ⊆ Rn is a domain (i.e. open and connected) and f : U → Rm is differentiable at all x ∈ U . As we have seen, the derivative map of f at x is a linear map of ∆x, i.e. f 0 (x) : Rn → Rm . Making the dependence on x explicit, we get a linear-map-valued function of x, i.e. f 0 : U → Hom [Rn , Rm ]. The derivative map of the first derivative is a linear map of say ∆x, i.e. f 00 (x) : Rn → Hom [Rn , Rm ]. We may think of this as a bilinear map of ∆x and ∆x, i.e. f 00 (x) : Rn × Rn → Rm . Similarly we see that the k-th derivative is a k-linear map f (k) (x) : (Rn )k → Rm . It turns out that these multilinear maps are symmetric. Cartesian representation: Writing ∆x = (∆x1 , ...∆xn ) we may represent the linear map f 0 (x) by multiplication with the derivative matrix (the matrix of partial derivatives, a.k.a. the Jacobian matrix) 0
f (x)(∆x) =
n X
fi (x)∆xi
i=1
Second derivative: For simplicity assume that n = 2 and m = 1. Taking the derivative we obtain f 00 (x)(∆x, ∆x) = (f 0 (x)(∆x))(∆x) = f 0 (x)(∆x)1 ∆x1 + f 0 (x)(∆x)2 ∆x2 = (f1 (x)∆x1 + f2 (x)∆x2 )1 ∆x1 + (f1 (x)∆x1 + f2 (x)∆x2 )∆x2 = f11 (x)∆x1 ∆x1 + f21 (x)∆x2 ∆x1 + f12 (x)∆x1 ∆x2 + f22 (x)∆x2 ∆x2 The matrix representing this bilinear map is the matrix of second partial derivatives of f . Note that this is a symmetric matrix. f11 (x) f12 (x) ∆x1 00 f (x)(∆x, ∆x) = (∆x1 , ∆x2 ) f21 (x) f22 (x) ∆x2 More generally with arbitrary n and m we can write 00
f (x)(∆x, ∆x) =
n X
0
f (x)(∆x)j ∆xj =
j=1
n n X X j=1
i=1
! fi (x)∆xi
∆xj = j
n X
fij (x)∆xi ∆xj
i,j=1
Higher derivatives: We can obtain similar formulas for higher derivatives with the symmetric tensor of higher partial derivatives. Taylor polynomials: Suppose f is k times differentiable at x. Then f (x + ∆x) is “approximated” (as ∆x → 0) by the k-th Taylor polynomial 1 1 1 f (x + ∆x) ≈ f (x) + f 0 (x)(∆x) + f 00 (x)(∆x, ∆x) + f 000 (x)(∆x, ∆x, ∆x) + ... f (k) (x)(∆x, ∆x, ...∆x) 2 6 k! For example in cartesian coordinates with n = 2 and m = 1 we get 1 1 f (x1 + ∆x1 , x2 + ∆x2 ) ≈ f (x) + f1 (x)∆x1 + f2 (x)∆x2 + f11 (x)∆x21 + f12 (x)∆x1 ∆x2 + f22 (x)∆x22 2 2 n X 1 +... fi i ...i (x)∆xi1 ∆xi2 ...∆xik k! i ,i ,...i =1 1 2 n 1
Higher derivatives as multilinear maps. Suppose U â R n is a domain (i.e. open and connected) and f : U â R m is differentiable at all x â U. As we have seen, the derivative map of f at x is a linear map of âx, i.e. f (x) : R n â R m . Making the dependence on x explicit, we get a linear-map-valued function of x, i.e. f : U âHom ...
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