Mathematics · Glossary

What is differentiable?

Also known as: differential · Jacobian matrix

Definition 15.1 University Mathematics — Year 2 · Chapter 15 — Differential Calculus

ff is differentiable at aa when there is a (continuous) linear map  ⁣dfa ⁣:RnRm\dd f_a \colon \R^n \to \R^m with

f(a+h)=f(a)+ ⁣dfa(h)+o(h)(h0).f(a + h) = f(a) + \dd f_a(h) + o(\norm h) \qquad (h \to 0).

The map  ⁣dfa\dd f_a, the differential of ff at aa, is unique; its matrix in the canonical bases is the Jacobian matrix Jf(a)=(fixj(a))J_f(a) = \bigl(\frac{\partial f_i}{\partial x_j}(a)\bigr). Differentiability implies continuity and the existence of all directional derivatives  ⁣dfa(v)=limt0f(a+tv)f(a)t\dd f_a(v) = \lim_{t\to0}\frac{f(a + tv) - f(a)}{t}; the converse fails (Exercise 15.2). For m=1m = 1,  ⁣dfa(h)=f(a),h\dd f_a(h) = \langle \nabla f(a), h\rangle: the gradient of Year 1, now understood as the vector representing the differential.

Examples

Example 15.4 (Radial functions, once and for all)

Let r(x)=x2r(x) = \norm x_2 on Rn{0}\R^n\setminus\{0\} and f=grf = g \circ r with gg a C1C^1 function of one variable. First, rr is differentiable away from 00: from r2=xi2r^2 = \sum x_i^2,

rxi=xir,i.e.r(x)=xx,\frac{\partial r}{\partial x_i} = \frac{x_i}{r}, \qquad\text{i.e.}\qquad \nabla r(x) = \frac{x}{\norm x} ,

the unit radial vector (differentiate r2r^2 and divide — or apply the chain rule to \sqrt{\cdot}). Then the chain rule gives, for every radial function,

f(x)=g(x)xx.\nabla f(x) = g'\bigl(\norm x\bigr)\,\frac{x}{\norm x} .

Worked instance: g(r)=1rg(r) = \frac1r yields 1x=xx3\nabla\frac{1}{\norm x} = -\frac{x}{\norm x^3}, the inverse-square field of gravitation and electrostatics — direction radial, magnitude 1x2\frac{1}{\norm x^2}. Closing insight: gradients of radial functions are radial because the level sets are spheres and the gradient is orthogonal to level sets; at x=0x = 0, by contrast, rr is not differentiable (no candidate linear map matches h\norm h from all directions) — smooth radial profiles need g(0)=0g'(0) = 0 to cross the origin gracefully.

Example 15.6 (A Lipschitz constant from the MVI)

Is f(x,y)=sinxsinyf(x, y) = \sin x\,\sin y Lipschitz on R2\R^2, and with which constant? Its gradient is f=(cosxsiny, sinxcosy)\nabla f = (\cos x\sin y,\ \sin x\cos y), of squared norm

cos2xsin2y+sin2xcos2ysin2y+cos2y1=1\cos^2x\sin^2y + \sin^2x\cos^2y \leq \sin^2 y + \cos^2y\cdot 1 = 1

(bound cos2x\cos^2x and sin2x\sin^2x by 11 separately), so  ⁣df(x,y)=f1\vertiii{\dd f_{(x,y)}} = \norm{\nabla f} \leq 1 everywhere, and Theorem 15.5 on the segment between any two points gives

f(b)f(a)ba2:\abs{f(b) - f(a)} \leq \norm{b - a}_2 :

ff is 11-Lipschitz, and the constant is sharp (near the origin, f(x,π2)=sinxf(x, \tfrac\pi2) = \sin x has slope 11). Closing insight: the mean value inequality converts a pointwise bound on the differential into a global modulus of continuity — the standard route to Lipschitz estimates in every dimension, and the engine inside Exercise 15.12.

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