Alphabeta Math
ExampleConstruction: AI-generatedVerification: AI-generatedPipeline-generatedjudge pass (gpt-6-luna)
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Kantorovich–Rubinstein duality for masses on two separated points

Example

Let X={a,b} have the two-point metric d(a,b)=D>0, so X is compact. For p,q∈[0,1], set μ=pδa+(1−p)δb and ν=qδa+(1−q)δb. Then W1X(μ,ν)=D∣p−q∣, and the supremum over 1-Lipschitz functions normalized by f(a)=0 has the same value.

Facts & Assumptions

Given: The finite metric space and probability measures are as in the Example.

[F2]

The Dirac set functions at a and b are probability measures; nonnegative finite linear combinations preserve countable additivity, and coefficients summing to one preserve total mass. Thus the displayed convex combinations have the stated atom masses and are probabilities. The Dirac set function at a point A Dirac set function is a probability measure Measures on sigma-algebras Probability measures and probability spaces

[F3]

W1X is the infimum of the integral of the metric over Borel probability couplings with the prescribed marginals. The coupling definition of the 1-Wasserstein cost on a compact metric space

[F4]

On a finite measurable space, a nonnegative simple function integrates as the finite sum of its values times the measures of its level sets; signed integrals are defined by positive and negative parts. The nonnegative Lebesgue integral Nonnegative simple measurable functions The integral of a nonnegative simple function The nonnegative integral agrees with the simple integral on simple functions Integrable real and complex functions, and their integrals

[F5]

A measure is countably additive on disjoint measurable sets; this implies finite additivity and finite subadditivity. Measures on sigma-algebras

Verification

1.1givenF1F2F3F4F5algebra

By [F1], X×X consists of four Borel singletons. Given any admissible Borel coupling γ, define πuv:=γ({(u,v)}) for u,v∈{a,b}. Finite additivity [F5] makes π a nonnegative matrix with total sum 1; its row and column sums are the masses of the marginals, namely (p,1−p) and (q,1−q) by [F2]. Thus p−q=πab−πba. By [F4], its cost is D(πab+πba)≥D∣p−q∣, so [F3] gives W1X(μ,ν)≥D∣p−q∣.

1.2givenF1F2F4algebrachoose

Every real-valued function f on the finite discrete set is Borel measurable, and f+ and f− are nonnegative simple functions. For either λ=μ or λ=ν, apply the nonnegative finite-sum formula [F4] separately to f+ and f− to get ∫f± dλ=f±(a)λ({a})+f±(b)λ({b})<∞. Subtracting these two finite quantities by the signed-integral definition in [F4] proves ∫f dλ=f(a)λ({a})+f(b)λ({b}). Using the atom masses [F2], the objective for a feasible f with f(a)=0 is therefore (p−q)(f(a)−f(b)). Since ∣f(a)−f(b)∣≤D, the objective is at most D∣p−q∣. If p≥q, choose f(b)=−D; if p<q, choose f(b)=D. Each choice is feasible and attains D∣p−q∣, including p=q.

2.1givenF1F3F4F5step 1.1constructalgebra

If p≥q, the matrix (q,p−q,0,1−p) is nonnegative, has the required row sums (p,1−p) and column sums (q,1−q), and costs D(p−q). If p<q, the matrix (p,0,q−p,1−q) is nonnegative, has the same required marginals, and costs D(q−p). Either matrix defines a Borel probability on the four-point set by assigning each singleton its matrix entry; countable additivity follows because only finitely many disjoint subsets can be nonempty, and [F5] gives finite additivity. Its row and column sums give the required marginals, and [F4] gives its stated cost. Thus [F3] yields W1X(μ,ν)≤D∣p−q∣, and step 1.1 gives equality.

3.1step 1.1step 1.2step 2.1∎

Steps 1.1 and 2.1 give the primal value D∣p−q∣, and step 1.2 gives the same dual supremum, including p=q and endpoints p,q∈{0,1}.

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