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A finite adaptive Euler–Maruyama budget returns a status
Statement
Assume AC and consider the budgeted procedure in
Euler–Maruyama on a predictable grid with bridge-refinement status with integer budget
, at most trial operations occur. The procedure returns
complete only after reaching ; if the budget is exhausted earlier it
returns inconclusive. Consequently its number of trial operations is an
integrable random variable with expectation at most . This is a finite
budget operation count, not an asymptotic cost estimate in terms of error.
Facts & Assumptions
Given: AC; the procedure, its finite work budget , and its rule that each trial operation consumes one unit.
Each predictable-grid update and each endpoint-refinement node trial consumes one unit; after units without reaching , the algorithm returns inconclusive. Euler–Maruyama on a predictable grid with bridge-refinement status
AC is inherited from the algorithm interface; the counter argument itself is choice-free. The Axiom of Choice
Proof
Before the first operation the counter is zero. Each operation increases it by exactly one, and no branch decreases it or runs without consuming a unit. Induction on the operation count therefore gives a counter between and throughout execution; the loop either reaches and returns complete, or reaches counter first and returns inconclusive by [F1].
Each trial state and finite queue is a measurable function of the initial data, Brownian increments and fresh Gaussian innovations: coefficient evaluation and the acceptance rule are Borel, and updates, splits and queue operations are measurable finite operations. Induction through the deterministic budget makes the event that trial is performed measurable for each . Thus is a random variable. It satisfies pointwise by step 1.1, so it is integrable and . If , no trial is performed and, since , the status is inconclusive. This proves the claim without asserting that completion occurs with positive probability.
Depends on
Used by
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Sources
- Simo Särkkä and Arno Solin, Applied Stochastic Differential Equations, Algorithm 8.1 (standard reference, not scraped)