Alphabeta Math
LemmaStatement: AI-adaptedProof: AI-adaptedPipeline-generatedjudge pass (gpt-5.6-terra)audited 2026-09-10
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Absolute real powers are Borel measurable and convex

Statement

For each real p≥1, the function ϕp:R→R defined by ϕp(t)=∣t∣p is finite, continuous, Borel measurable and convex. Here 0p=0.

Facts & Assumptions

Given: A real exponent p≥1, with the real-power convention 0p=0.

[F1]

For positive bases up=exp⁡(plog⁡u); 0p=0 for p>0. (Real powers for positive bases, with the zero-base positive-exponent convention)

[F2]

On positive bases u↦up is continuous with derivative pup−1. (Continuity and derivatives of positive-base real powers)

[F4]

A twice differentiable function with nonnegative second derivative on an open interval is convex. (A twice-differentiable function on an open interval is convex if and only if its second derivative is nonnegative)

[F7]
[F9]

Two equal bounding limits force the intermediate limit. (If f≤g≤h near c and f and h have the same limit at c, then so does g)

[F10]

∣a+b∣≤∣a∣+∣b∣. (The triangle inequality)

[F11]

Absolute value is nonnegative and multiplicative. (Basic properties of the absolute value)

[F12]

Convexity is the convex-combination inequality for all weights in [0,1]. (Convex, strictly convex, concave, strictly concave, and midpoint-convex real functions on an interval)

[F14]

Continuous preimages of Borel sets are Borel. (A continuous map has Borel preimages of Borel sets)

[F15]

Measurability means that every measurable target preimage is measurable. (A measurable function between measurable spaces)

[F16]

For u>0, exp⁡(log⁡u)=u by the inverse definition. (The natural logarithm as the inverse of the exponential function)

Proof

technique · direct
1.1F2F3F4F5

For p>1 and u>0, differentiation gives h′(u)=pup−1 and h′′(u)=p(p−1)up−2≥0 for h(u)=up. Thus h is nondecreasing and convex on (0,∞). The derivative and second-derivative hypotheses hold at every positive u.

1.2F1F6F7F8F9F16

For 0<u≤1, log⁡u≤0, hence plog⁡u≤log⁡u and 0<up=exp⁡(plog⁡u)≤exp⁡(log⁡u)=u. The last equality is the inverse identity [F16]. The squeeze theorem gives up→0 as u↓0. With h(0)=0, this extends h continuously to [0,∞).

2.1step 1.1step 1.2

For a,b≥0 and 0≤λ≤1, apply positive-half-line convexity to a+ε,b+ε and let ε↓0 using step 1.2: h(λa+(1−λ)b)≤λh(a)+(1−λ)h(b). The inequality remains valid at weights zero and one, where it is equality. Monotonicity extends to zero because h≥0=h(0).

3.1step 2.1F10F11F12

For real x,y, [F10]–[F11] give ∣λx+(1−λ)y∣≤λ∣x∣+(1−λ)∣y∣. Apply monotonicity and then step 2.1 to get ∣λx+(1−λ)y∣p≤λ∣x∣p+(1−λ)∣y∣p. For p=1 the same inequality is already precisely the triangle inequality with the scalar absolute values evaluated. Thus [F12] proves convexity for every p≥1.

4.1step 1.1step 1.2F1F13F14F15∎

The function is finite by [F1]. Continuity of absolute value [F13] and continuity of h (steps 1.1–1.2, or the identity for p=1) imply continuity of h(∣t∣): choose an output tolerance for h at ∣t∣ and then the corresponding input tolerance for absolute value. Consequently all Borel preimages are Borel by [F14], which is exactly [F15].

Source notes

Durrett Theorem 4.1.11, printed pp.211–212, and van der Vaart Lemma 1.9(vii), printed p.4, use this power in the contraction argument. The calculus and endpoint proof is supplied here from the explicitly cited local real-analysis results.

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Sources