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Gruber P. Convex and Discrete Geometry

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1 <strong>Convex</strong> Functions of One Variable 11<br />

The above definition of second-order differentiability is slightly weaker than<br />

the common definition. In this way it still embodies the main idea of the common<br />

second-order differentiability <strong>and</strong> has the advantage that it applies to general convex<br />

functions as the following result shows:<br />

Theorem 1.7. Let I be open <strong>and</strong> f : I → R convex. Then f is twice differentiable<br />

almost everywhere on I . Moreover, for almost every x ∈ I,<br />

f (y) = f (x) + f ′ (x)(y − x) + 1<br />

2 f ′′ (x)(y − x) 2 + o(|y − x| 2 ) as y → x, y ∈ I.<br />

Proof. We first state two well-known theorems of Lebesgue, see [499].<br />

(9) A non-decreasing real function on I is almost everywhere differentiable.<br />

(10) The derivative of an absolutely continuous function g : J → R exists<br />

almost everywhere on J, is Lebesgue integrable <strong>and</strong>, for x ∈ J,<br />

�y<br />

g(y) = g(x) + g ′ (t) dt for y ∈ J.<br />

By Theorem 1.4 above <strong>and</strong> (9), there are a countable set M <strong>and</strong> a set N of measure<br />

0, both in I , such that the following statements hold: f ′ (x) exists <strong>and</strong> equals<br />

f ′ − (x) for each x ∈ I \M <strong>and</strong> f ′ − exists on I <strong>and</strong> is non-decreasing, <strong>and</strong> f ′′<br />

− (x) exists<br />

for each x ∈ I \N. Thus,<br />

(11) f ′′<br />

− (x) = lim<br />

y→x<br />

= lim<br />

y→x<br />

y∈I \M<br />

x<br />

f ′ − (y) − f ′ − (x)<br />

y − x<br />

f ′ (y) − f ′ (x)<br />

y − x<br />

= lim<br />

y→x<br />

y∈I \M<br />

f ′ − (y) − f ′ − (x)<br />

y − x<br />

= f ′′ (x) for x ∈ I \(M ∪ N),<br />

concluding the proof of the first assertion of the theorem.<br />

To show the second assertion, note that (11) yields the following:<br />

(12) f ′ (y) = f ′ (x) + f ′′ (x)(y − x) + o(|y − x|)<br />

as y → x, y ∈ I \M for x ∈ I \(M ∪ N).<br />

Since by Theorem 1.1 f is absolutely continuous, Proposition (10) shows that one<br />

may integrate (12) from x to y to obtain<br />

f (y) = f (x) + f ′ (x)(y − x) + 1<br />

2 f ′′ (x)(y − x) 2 + o(|y − x| 2 )<br />

as y → x, y ∈ I \M for x ∈ I \(M ∪ N).<br />

Now note that the continuity of both sides of this equality permits us to cancel the<br />

restriction that y �∈ M. ⊓⊔<br />

Theorem 1.5 says that for any convex function f the differentiability of f implies<br />

that f ′ is continuous, i.e. f is of class C 1 . Examples show that there is no analogous<br />

result for f ′′ :If f ′′ exists it need not be continuous.

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