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Integral Jensen inequality for finite-dimensional convex losses

Statement

Assume the Axiom of Choice and Countable Choice. Let d≥1 and let C be a nonempty closed convex subset of Rd, with closedness in the Euclidean topology (Interior, closure, boundary, limit point, isolated point and dense subset of a metric space) and convexity as in A convex subset of Rm contains every line segment between two of its points. Let μ be a probability measure on its Borel sets, and assume ∫C∥a∥2 μ(da)<∞, where ∥⋅∥2 is the Euclidean norm from The Euclidean inner product ⟨x,y⟩=∑k<nxkyk on Rn. Define the barycenter b=∫Ca μ(da) coordinatewise and assume b∈C. If f:C→[0,+∞] is Borel measurable and convex, then f(b)≤∫Cf(a) μ(da). Convexity for this possibly extended-valued f uses the extended-functional convention of Convex and strictly convex functionals on a convex subset of a real vector space, including its endpoint convention for 0⋅(+∞). On a convex restriction where f is finite, this inequality reduces to the real-valued Euclidean convention of Convex and strictly convex functions on Euclidean convex sets, as used in the subgradient step of the proof. The barycenter is the vector of coordinate integrals.

Facts & Assumptions

Given: The statement's C, μ, f, and barycenter b.

[A1]

Assume the Axiom of Choice and Countable Choice, as required by the supporting-hyperplane and subgradient suppliers (The Axiom of Choice, The Axiom of Countable Choice (ACω)).

[F1]

A point outside a nonempty closed convex set is strictly separated from it by a hyperplane (A point outside a nonempty closed convex set is strictly separated from it).

[F2]

A boundary point of a nonempty convex set has a supporting hyperplane (Every boundary point belonging to a nonempty Euclidean convex set has a supporting hyperplane).

[F3]

The supplier statement gives common ordinary interior and boundary; its proof steps 2.1--3.1 also prove that a convex set and its closure have the same relative interior in their affine hull (A convex set and its closure have the same interior and boundary).

[F4]

A finite convex function has a subgradient at every interior point of its domain (A convex function has a subgradient at every interior point of its domain); a subgradient satisfies the global supporting inequality of Subgradients and the subdifferential of a convex function.

[F5]

A nonnegative measurable function with integral zero vanishes almost everywhere (A nonnegative measurable function has integral 0 exactly when it vanishes almost everywhere).

[F6]

Coordinate integrals are linear and give the barycenter, while the expectation of an integrable real function is the difference of its positive- and negative-part integrals (Expectation of a nonnegative or integrable random variable, The Lebesgue integral is linear on L1(μ)).

[F7]

The nonnegative integral is monotone and positively homogeneous, so a pointwise nonnegative simple minorant gives a lower bound on the integral (Monotonicity and nonnegative homogeneity of the nonnegative integral).

[F8]

The nonnegative integral is additive on finite sums (Additivity of the nonnegative Lebesgue integral).

Proof

technique · direct
1.1F7given

If ∫f dμ=+∞, the conclusion holds because f(b)∈[0,+∞]. Otherwise let A:={a∈C:f(a)=+∞}. For every integer n≥1, the simple function n1A is bounded above by f, so [F7] gives nμ(A)=∫n1A dμ≤∫f dμ<∞. If μ(A)>0, these lower bounds are unbounded as n→∞ (and are already infinite if μ(A)=+∞), a contradiction. Thus E:=C∖A={a:f(a)<∞} has full μ-measure. Let S be the support of μ, the set of points every relative open neighbourhood of which has positive measure. Since C has a countable base inherited from rational balls in Rd, the complement of S is a countable union of open null basic sets, so μ(S)=1. Every neighbourhood of a point of S meets E, hence E∩S is dense in S.

2.1F1F5F6step 1.1

Let C0 be the closure of the set of finite convex combinations of points of S. Then C0⊆C. If b∉C0, [F1] gives a linear functional u and a real α with ⟨u,y⟩≤α<⟨u,b⟩ for all y∈C0. The function ψ(a)=α−⟨u,a⟩ is integrable by the first-moment assumption and is nonnegative on the full-measure support S. Hence ψ−=0 almost everywhere, so [F5] gives ∫ψ− dμ=0 and ∫ψ dμ=∫ψ+ dμ≥0. By [F6] and the coordinate definition of b, this says α−⟨u,b⟩≥0, contradicting the strict separation. Thus b∈C0.

3.1A1F2F5F6step 2.1

If b were on the relative boundary of C0, [F2], applied in its affine hull, would give a nonzero supporting normal v there with ⟨v,y−b⟩≤0 for all y∈C0. Set u(a)=⟨v,b−a⟩. The first-moment assumption makes u integrable, and [F6] gives ∫u dμ=0. Since u≥0 on the full-measure support S, its negative part u− vanishes almost everywhere; [F5] gives ∫u− dμ=0. The signed-integral definition in [F6] then gives ∫u+ dμ=0, so [F5] implies u+=0 almost everywhere and therefore u=0 almost everywhere. By continuity of u and the definition of support, every point of S must lie in the hyperplane ⟨v,y−b⟩=0: otherwise a neighborhood of a support point outside it would have positive measure on which u≠0. Its closed convex hull C0 would lie in that hyperplane, contradicting that v is nonzero in the direction space of its affine hull. Hence b is in the relative interior of C0.

4.1A1F3F4step 1.1step 3.1

Put C1=conv⁡(E∩S), where conv⁡ denotes finite convex combinations. The extended-functional convexity inequality makes f finite on C1; on these finite values it is the real-valued Euclidean convexity of Convex and strictly convex functions on Euclidean convex sets. Density from step 1.1 gives C1‾=C0. By [F3], b is in the relative interior of C1. If this affine hull has dimension zero, then μ is concentrated at b and the claim is equality. Otherwise identify the affine hull isometrically with Rk; [F4] applied to f∣C1 gives a subgradient v at b, so f(y)≥f(b)+⟨v,y−b⟩ for every y∈C1.

5.1F5F6F8step 1.1step 4.1∎

Let ℓ(a)=f(b)+⟨v,a−b⟩ and w=(f−ℓ)1E, defining w=0 on Ec. The function w is integrable because f1E has finite nonnegative integral and ℓ is integrable by the first-moment assumption. The supporting inequality gives w≥0 on E∩S, a full-measure set; thus w−=0 almost everywhere. By [F5], ∫w− dμ=0, so [F6] gives ∫w dμ=∫w+ dμ≥0. Linearity [F6] now yields ∫f1E dμ≥∫ℓ1E dμ. Also f=f1E+f1Ec, with f1Ec=0 almost everywhere; [F5] and additivity [F8] give ∫f dμ=∫f1E dμ. The integrable function ℓ1Ec is likewise zero almost everywhere, so [F5] applied to its positive and negative parts and linearity [F6] give ∫ℓ dμ=∫ℓ1E dμ. Finally [F6] and the barycenter definition yield ∫ℓ dμ=f(b)+⟨v,b−b⟩=f(b). This proves the claim in the finite-integral case; the infinite-integral case was handled in step 1.1.

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