TheoremBase

Comparing the convexity inequality along a short segment with the first-order expansion shows the gradient is a subgradient; comparing a subgradient inequality with the expansion in the direction of the difference shows no other vector is.

Proof

By Differentiability at a Point for Functions Between Euclidean Spaces (iii), ff is differentiable at xx with derivative vector ww for some w∈Rnw\in\mathbb{R}^{n}, and w=βˆ‡f(x)w=\nabla f(x) by The Derivative Is the Jacobian Matrix, and Is Unique (i). Thus for every real Ξ΅>0\varepsilon>0 there is a real Ξ΄>0\delta>0 such that every h∈Rnh\in\mathbb{R}^{n} with βˆ₯hβˆ₯<Ξ΄\lVert h\rVert<\delta satisfies x+h∈Ux+h\in U and

∣f(x+h)βˆ’f(x)βˆ’wβ‹…hβˆ£β‰€Ξ΅βˆ₯hβˆ₯.(1)\bigl|f(x+h)-f(x)-w\cdot h\bigr|\le\varepsilon\lVert h\rVert. \tag{1}

We first show that wβˆˆβˆ‚f(x)w\in\partial f(x). Let y∈Uy\in U with yβ‰ xy\neq x and put h=yβˆ’xh=y-x. Let Ξ΅>0\varepsilon>0 be given, and choose Ξ΄\delta for this Ξ΅\varepsilon as in (1); then put s=min⁑{1,Ξ΄/(2βˆ₯hβˆ₯)}s=\min\{1,\delta/(2\lVert h\rVert)\}, so that 0<s≀10<s\le1 and βˆ₯shβˆ₯<Ξ΄\lVert sh\rVert<\delta (Properties of the Euclidean Norm on Rn\mathbb{R}^n: Square, Distance, Vanishing, Coordinate Bounds, Homogeneity, Triangle Inequalities and Expansion of a Square (v)). Since x+sh=(1βˆ’s)x+syx+sh=(1-s)x+sy, the convexity inequality of Convex Function on a Convex Subset of Rn\mathbb{R}^n (i) and (1) for shsh give

s wβ‹…hβˆ’Ξ΅sβˆ₯hβˆ₯≀f(x+sh)βˆ’f(x)≀s(f(y)βˆ’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).

Dividing by s>0s>0 yields wβ‹…hβˆ’Ξ΅βˆ₯hβˆ₯≀f(y)βˆ’f(x)w\cdot h-\varepsilon\lVert h\rVert\le f(y)-f(x), and since Ξ΅>0\varepsilon>0 was arbitrary,

f(y)β‰₯f(x)+wβ‹…(yβˆ’x).f(y)\ge f(x)+w\cdot(y-x).

For y=xy=x this holds trivially. So ww is a subgradient of ff at xx, and wβˆˆβˆ‚f(x)w\in\partial f(x) by Subgradients and the Subdifferential of a Real-Valued Function on a Subset of Rn\mathbb{R}^n (ii).

It remains to show that there is no other subgradient. Let aβˆˆβˆ‚f(x)a\in\partial f(x) and v=aβˆ’wv=a-w, and suppose that vβ‰ 0v\neq0, so βˆ₯vβˆ₯>0\lVert v\rVert>0. Take Ξ΅=βˆ₯vβˆ₯/2\varepsilon=\lVert v\rVert/2 in (1), choose its Ξ΄\delta, and then a real s>0s>0 with sβˆ₯vβˆ₯<Ξ΄s\lVert v\rVert<\delta. Then x+sv∈Ux+sv\in U, and the subgradient inequality for aa at x+svx+sv, together with (1) for svsv, gives

s aβ‹…v≀f(x+sv)βˆ’f(x)≀s wβ‹…v+Ξ΅sβˆ₯vβˆ₯.s\,a\cdot v\le f(x+sv)-f(x)\le s\,w\cdot v+\varepsilon s\lVert v\rVert .

Dividing by ss and using bilinearity of the dot product,

βˆ₯vβˆ₯2=vβ‹…v=(aβˆ’w)β‹…v≀Ρβˆ₯vβˆ₯=βˆ₯vβˆ₯2/2,\lVert v\rVert^{2}=v\cdot v=(a-w)\cdot v\le\varepsilon\lVert v\rVert=\lVert v\rVert^{2}/2,

which is impossible. Hence a=wa=w, and βˆ‚f(x)={βˆ‡f(x)}\partial f(x)=\{\nabla f(x)\}.

Citations

Loading…

Dependencies

Uses0

Loading…

Comments

Log in to comment.

Loading…