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MLMC work bound for bounded finite-state Poisson tau-leaping

Statement

Assume Countable Choice and Dependent Choice, and the finite-state hypotheses of Weak order one for finite-state Poisson tau-leaping with an exit bound and Order-one correction variance for coupled Poisson tau-leap levels, with bounded payoff g. Use levels hℓ=T2−ℓ and couple adjacent Poisson tau-leap paths by the shared-minimum construction. Under the work convention that each channel Poisson draw, propensity evaluation, and stoichiometric update has unit cost, for each ℓ≥1 one correction sample costs at most Chℓ−1: there are T/hℓ fine half intervals, each using at most 3R shared and residual Poisson draws and O(R) propensity and update work. The base-level sample uses one path and costs O(R), which is O(h0−1) for fixed T. The level bias is O(hL) and the correction variance is O(hℓ). Therefore, for all sufficiently small ϵ>0, deterministic sample counts in the generic multilevel estimator can achieve mean square error at most ϵ2 with work O ⁣(ϵ−2(log⁡ϵ−1)2). This conclusion applies only to the explicit Poisson leap and the bounded finite-state hypotheses above. It is not a correctness or error guarantee for the guarded binomial algorithm Critical-reaction binomial guarded tau-leaping.

Facts & Assumptions

Given: The geometric hierarchy, adjacent-level coupling, finite-state error hypotheses, and unit-cost convention in the Statement.

[F1]

Weak order one for finite-state Poisson tau-leaping with an exit bound: the payoff bias at level ℓ is at most Cbhℓ.

[F2]

Order-one correction variance for coupled Poisson tau-leap levels: the adjacent-level correction variance is at most Cvhℓ.

[F3]

Geometric MLMC hierarchy and coupled corrections and Expectation, variance, and MSE of a finite MLMC estimator: the finite multilevel estimator telescopes in expectation and has variance equal to the sum of level correction variances divided by their sample counts.

[F4]

MLMC cost complexity under bias, variance, and work rates: if bias is O(hα), correction variance is O(hβ), and level cost is O(h−1), the stated MLMC work upper bounds apply.

[A1]

Countable Choice is assumed through the exact finite-state chain used in [F1]. The Axiom of Countable Choice (ACω)

[A2]

Dependent Choice is assumed through the countable random input stream used to define that chain. The axiom of dependent choice: a relation in which every element is related to something admits an N-indexed chain

Proof

Given: Countable Choice, Dependent Choice, and the assumptions above.

1.1givenalgebra

For ℓ≥1, the fine and coarse paths use T/hℓ and T/hℓ−1 steps, respectively; both ratios are integers on the dyadic hierarchy. On each fine half interval, each channel uses one shared and two residual Poisson counts, hence at most 3R scalar draws and O(R) propensity and update work; the two coarse count vectors are accumulated over each coarse interval. Thus the correction cost is O(TR/hℓ)=O(hℓ−1) for fixed T. At level zero, a single path takes one step and uses O(R) work; since h0=T>0 is fixed, this is also O(h0−1) after changing the constant.

2.1F1F2F3F4A1A2step 1.1∎

By [F1] the bias exponent in [F4] is α=1. By [F2], every adjacent-level variance for ℓ≥1 is O(hℓ). At level zero, Δ0=g(YT), so Var⁡(Δ0)≤E[g(YT)2]≤∥g∥∞2; since h0=T>0, increasing the variance constant gives the required O(h0) bound there as well. Thus the variance exponent for all levels is β=1. Apply [F4] with refinement factor 2 and these exponents. Its β=1 case gives O(ϵ−2(log⁡ϵ−1)2) work while controlling the MSE by [F3].

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