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A discontinuous payoff with bias order one and variance order one-half

Example

Fix an integer M≥2, put hℓ=M−ℓ, and choose c∈(0,1]. Let X have density fc(x)=12(1+cx) on [−1,1], let S be an independent Rademacher variable, and use the discontinuous payoff φ(x)=1{x≥0}. The target law is that of P=φ(X), and for each ℓ≥0 the level law is that of Pℓ=φ(X+hℓS). Couple adjacent level laws using the same pair (X,S) within each coupling.

For each correction sample, draw a fresh independent pair (X,S) with the stated law; within each positive-level correction use that same pair for the fine and coarse payoffs, and take all samples in distinct level blocks independently as required by the definition. This gives an infinite compatible family whose finite prefixes are MLMC hierarchies. The example has bias order α=1 and single-correction variance order β=12 for positive levels. The level-zero correction is bounded and h0=1, so enlarge the variance constant to include it. One sample costs O(1), so it also satisfies the abstract theorem's upper work assumption O(hℓ−1). Substituting that conservative work rate into the theorem gives the guarantee O(ϵ−5/2); the example does not claim this is a sharp cost for its constant-work evaluator.

Facts & Assumptions

Given: An integer M≥2, c∈(0,1], the density and payoff above, independence of X and S, and the common-pair coupling across each adjacent level.

[F1]

Geometric MLMC hierarchy and coupled corrections: adjacent corrections may use any joint law with the stated fine and coarse marginals; here the same (X,S) supplies that coupling.

[F2]

MLMC cost complexity under bias, variance, and work rates: with α=1, β=12, and work exponent γ=1, the guaranteed cost exponent is 2+(1−β)/α=5/2.

Verification

1.1F1givenalgebra

This is a hierarchy of the form in [F1]. The density is nonnegative on [−1,1] because c≤1, and its integral is 12∫−11(1+cx) dx=1. The target mean is EP=12+c/4. Writing δ=hℓ and averaging over S=±1 gives EPℓ=12(P(X≥−δ)+P(X≥δ))=EP−cδ24. Thus ∣EPℓ−EP∣=chℓ/4, so the bias exponent is α=1.

1.2F1givenalgebra

For ℓ≥1, put δf=hℓ and δc=hℓ−1=M δf. The correction Δℓ=Pℓ−Pℓ−1 is +1 when S=−1 and δf≤X<δc, is −1 when S=+1 and −δc≤X<−δf, and is zero otherwise. Integrating the density on those two intervals and averaging over the two signs gives P(Δℓ≠0)=δc−δf2=M−12hℓ=:pℓ. The level-0 correction is bounded, so it has finite variance as well.

2.1givenstep 1.2algebra

The two signed crossing probabilities give EΔℓ=c(M−1)hℓ/4. Since Δℓ2=1{Δℓ≠0}, Var⁡(Δℓ)=pℓ−(EΔℓ)2. For ℓ≥1, hℓ≤1/M and (EΔℓ)2pℓ=c2(M−1)2hℓ3/28(M−1)≤c2(M−1)(M+1)28M3/2≤c24≤14. The last inequality follows from (M−1)(M+1)2≤2M3/2 for M≥2. Hence 34pℓ≤Var⁡(Δℓ)≤pℓ, which proves the exact order β=12.

3.1F2step 1.1step 2.1algebra∎

The sample evaluator uses a constant number of draws, so its cost is O(1)≤O(hℓ−1) because 0<hℓ≤1. Substituting α=1, β=12, γ=1 into [F2] gives the stated O(ϵ−5/2) upper guarantee under that work-rate bound.

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