By Differentiability at a Point for Functions Between Euclidean Spaces (iii) , f f f is differentiable at x x x with derivative vector w w w for some w β R n w\in\mathbb{R}^{n} w β R n , and w = β f ( x ) w=\nabla f(x) w = β f ( x ) by The Derivative Is the Jacobian Matrix, and Is Unique (i) . Thus for every real Ξ΅ > 0 \varepsilon>0 Ξ΅ > 0 there is a real Ξ΄ > 0 \delta>0 Ξ΄ > 0 such that every h β R n h\in\mathbb{R}^{n} h β R n with β₯ h β₯ < Ξ΄ \lVert h\rVert<\delta β₯ h β₯ < Ξ΄ satisfies x + h β U x+h\in U x + h β U and
β£ f ( x + h ) β f ( x ) β w β
h β£ β€ Ξ΅ β₯ h β₯ . (1) \bigl|f(x+h)-f(x)-w\cdot h\bigr|\le\varepsilon\lVert h\rVert. \tag{1} β f ( x + h ) β f ( x ) β w β
h β β€ Ξ΅ β₯ h β₯ . ( 1 )
We first show that w β β f ( x ) w\in\partial f(x) w β β f ( x ) . Let y β U y\in U y β U with y β x y\neq x y ξ = x and put h = y β x h=y-x h = y β x . Let Ξ΅ > 0 \varepsilon>0 Ξ΅ > 0 be given, and choose Ξ΄ \delta Ξ΄ for this Ξ΅ \varepsilon Ξ΅ as in (1); then put s = min β‘ { 1 , Ξ΄ / ( 2 β₯ h β₯ ) } s=\min\{1,\delta/(2\lVert h\rVert)\} s = min { 1 , Ξ΄ / ( 2 β₯ h β₯)} , so that 0 < s β€ 1 0<s\le1 0 < s β€ 1 and β₯ s h β₯ < Ξ΄ \lVert sh\rVert<\delta β₯ s h β₯ < Ξ΄ (Properties of the Euclidean Norm on R n \mathbb{R}^n R n : Square, Distance, Vanishing, Coordinate Bounds, Homogeneity, Triangle Inequalities and Expansion of a Square (v) ). Since x + s h = ( 1 β s ) x + s y x+sh=(1-s)x+sy x + s h = ( 1 β s ) x + sy , the convexity inequality of Convex Function on a Convex Subset of R n \mathbb{R}^n R n (i) and (1) for s h sh s h give
s β w β
h β Ξ΅ s β₯ h β₯ β€ f ( x + s h ) β f ( x ) β€ s ( f ( y ) β f ( x ) ) . s\,w\cdot h-\varepsilon s\lVert h\rVert\le f(x+sh)-f(x)\le s\bigl(f(y)-f(x)\bigr). s w β
h β Ξ΅ s β₯ h β₯ β€ f ( x + s h ) β f ( x ) β€ s ( f ( y ) β f ( x ) ) .
Dividing by s > 0 s>0 s > 0 yields w β
h β Ξ΅ β₯ h β₯ β€ f ( y ) β f ( x ) w\cdot h-\varepsilon\lVert h\rVert\le f(y)-f(x) w β
h β Ξ΅ β₯ h β₯ β€ f ( y ) β f ( x ) , and since Ξ΅ > 0 \varepsilon>0 Ξ΅ > 0 was arbitrary,
f ( y ) β₯ f ( x ) + w β
( y β x ) . f(y)\ge f(x)+w\cdot(y-x). f ( y ) β₯ f ( x ) + w β
( y β x ) .
For y = x y=x y = x this holds trivially. So w w w is a subgradient of f f f at x x x , and w β β f ( x ) w\in\partial f(x) w β β f ( x ) by Subgradients and the Subdifferential of a Real-Valued Function on a Subset of R n \mathbb{R}^n R n (ii) .
It remains to show that there is no other subgradient. Let a β β f ( x ) a\in\partial f(x) a β β f ( x ) and v = a β w v=a-w v = a β w , and suppose that v β 0 v\neq0 v ξ = 0 , so β₯ v β₯ > 0 \lVert v\rVert>0 β₯ v β₯ > 0 . Take Ξ΅ = β₯ v β₯ / 2 \varepsilon=\lVert v\rVert/2 Ξ΅ = β₯ v β₯ /2 in (1), choose its Ξ΄ \delta Ξ΄ , and then a real s > 0 s>0 s > 0 with s β₯ v β₯ < Ξ΄ s\lVert v\rVert<\delta s β₯ v β₯ < Ξ΄ . Then x + s v β U x+sv\in U x + s v β U , and the subgradient inequality for a a a at x + s v x+sv x + s v , together with (1) for s v sv s v , gives
s β a β
v β€ f ( x + s v ) β f ( x ) β€ s β w β
v + Ξ΅ s β₯ v β₯ . s\,a\cdot v\le f(x+sv)-f(x)\le s\,w\cdot v+\varepsilon s\lVert v\rVert . s a β
v β€ f ( x + s v ) β f ( x ) β€ s w β
v + Ξ΅ s β₯ v β₯ .
Dividing by s s s and using bilinearity of the dot product,
β₯ v β₯ 2 = v β
v = ( a β w ) β
v β€ Ξ΅ β₯ v β₯ = β₯ v β₯ 2 / 2 , \lVert v\rVert^{2}=v\cdot v=(a-w)\cdot v\le\varepsilon\lVert v\rVert=\lVert v\rVert^{2}/2, β₯ v β₯ 2 = v β
v = ( a β w ) β
v β€ Ξ΅ β₯ v β₯ = β₯ v β₯ 2 /2 ,
which is impossible. Hence a = w a=w a = w , and β f ( x ) = { β f ( x ) } \partial f(x)=\{\nabla f(x)\} β f ( x ) = { β f ( x )} .