f is differentiable at a when there is a (continuous) linear map dfa:Rn→Rm with
f(a+h)=f(a)+dfa(h)+o(∥h∥)(h→0).
The map dfa, the differential of f at a, is unique; its matrix in the canonical bases is the Jacobian matrixJf(a)=(∂xj∂fi(a)). Differentiability implies continuity and the existence of all directional derivatives dfa(v)=limt→0tf(a+tv)−f(a); the converse fails (Exercise 15.2). For m=1, dfa(h)=⟨∇f(a),h⟩: 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)=∥x∥2 on Rn∖{0} and f=g∘r with g a C1 function of one variable. First, r is differentiable away from 0: from r2=∑xi2,
∂xi∂r=rxi,i.e.∇r(x)=∥x∥x,
the unit radial vector (differentiate r2 and divide — or apply the chain rule to ⋅). Then the chain rule gives, for every radial function,
∇f(x)=g′(∥x∥)∥x∥x.
Worked instance: g(r)=r1 yields ∇∥x∥1=−∥x∥3x, the inverse-square field of gravitation and electrostatics — direction radial, magnitude ∥x∥21. Closing insight: gradients of radial functions are radial because the level sets are spheres and the gradient is orthogonal to level sets; at x=0, by contrast, r is notdifferentiable (no candidate linear map matches ∥h∥ from all directions) — smooth radial profiles need g′(0)=0 to cross the origin gracefully.
Example 15.6(A Lipschitz constant from the MVI)
Is f(x,y)=sinxsinyLipschitz on R2, and with which constant? Its gradient is ∇f=(cosxsiny,sinxcosy), of squared norm
cos2xsin2y+sin2xcos2y≤sin2y+cos2y⋅1=1
(bound cos2x and sin2x by 1 separately), so df(x,y)=∥∇f∥≤1 everywhere, and Theorem 15.5 on the segment between any two points gives
∣f(b)−f(a)∣≤∥b−a∥2:
f is 1-Lipschitz, and the constant is sharp (near the origin, f(x,2π)=sinx has slope 1). 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.