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Moment-weighted local defect bounds for BAOAB
Statement
Let be globally Lipschitz, have linear growth , and have bounded first and second derivatives. Fix the model data in Underdamped Langevin equation and canonical Gibbs density, a finite starting state , and . Couple the exact SDE and one BAOAB step by the same Brownian increment, taking the BAOAB OU noise to be the standardized exact OU integral from Exact Ornstein–Uhlenbeck momentum transition and covariance. Put and with . There are remainders such that and, conditionally on the starting sigma algebra, For fixed Brownian noise in the step, the BAOAB residual also satisfies for all starting states . Constants depend on the model parameters and force bounds, not on the starting state or .
Facts & Assumptions
Given: The model and BAOAB definitions, a finite starting state measurable at time , , and a Brownian motion relative to the filtration.
Underdamped Langevin equation and canonical Gibbs density and Itô SDE data and strong solution: , the diffusion matrix is the constant , and the solution has the usual adapted It^o integral representation.
BAOAB underdamped Langevin splitting: the B/A/O/A/B step consists of two force kicks, two linear drifts, and the exact OU update.
Exact Ornstein–Uhlenbeck momentum transition and covariance: with , the OU noise is , independent of , and has second moment .
Deterministic Ito integrals are Gaussian: deterministic It^o integrals are centered Gaussian with covariance equal to the integral of the product of their integrands; in particular has mean zero and second moment at most .
Global strong well-posedness for Lipschitz Itô SDEs: for a square-integrable initial state measurable at the starting time and independent of future Brownian increments, globally Lipschitz, linearly growing coefficients give a unique global strong solution and finite second moments on finite intervals.
Multidimensional Ito formula for Brownian-driven processes: It^o's formula applies to linear momentum coordinate functions; the position path is continuously differentiable with , so the ordinary chain rule applies to when .
The Axiom of Choice: AC is inherited through [F1]–[F6]; the estimates use no new selection.
Proof
Given: The assumptions above.
First suppose . Shift time to and use with filtration . Brownian motion relative to this filtration has future increments independent of , so the initial state has the independence required in [F5]. The drift is globally Lipschitz with linear growth and is constant; [F5] therefore supplies the exact solution and finite second moments used in the following estimates. We first establish the estimates under this extra integrability condition, then remove it.
Set , , , and . Writing the BAOAB stages from [F2] gives [F1, F2, F3, F4, given, algebra] Subtracting yields Since and , the deterministic terms have size at most . The displayed formula and linear growth give and . Lipschitz continuity of and [F4] therefore give and .
For two starting states driven by the same , subtract the expression for in step 1.2. The result and the Lipschitz bound on give . Subtracting the two formulas then gives . The formula gives the same bound for its linear and terms; its last force difference is bounded by . Combining components proves .
With from step 1.2, the exact residual is . For , the integral equation, linear growth of , Cauchy–Schwarz in time, and [F4] give, with , [A1, F1, F4, F5, F6, step 1.2] Set with the constant enlarged so . Then almost everywhere, so and for . Substitution in the integral equation gives , and Cauchy–Schwarz in time gives . For its mean, the existence of the second derivative makes continuous, so . The ordinary chain rule along and the linear momentum equation [F6] give , where and . Bounded and linear growth of give . The constant-diffusion integral has conditional mean zero, so the preceding moment bound yields ; integration yields .
To remove the temporary assumption, put and . Apply [F5] to each bounded start with the same Brownian driver, obtaining . On , for the two initial values agree and the constant diffusion cancels from their integral equations. Thus ; the integrating-factor argument of step 2.2 with zero initial bound gives equality throughout the interval. Intersect the countably many full events and paste the paths on , with . These starting-time measurable events cover almost surely, so the pasted process is adapted and continuous and satisfies the additive-noise integral equation. The same difference argument proves uniqueness. The BAOAB outputs and both residuals likewise agree on with their truncated-start versions. Multiplying their conditional estimates from steps 1.2 and 2.2 by therefore gives the stated bounds on with the original and the same constants; let . For a merely finite random start, a signed conditional mean here denotes the consistent pasting of on ; each truncated residual is integrable by the bounds already proved. Conditional second moments are the corresponding nonnegative conditional expectations and are finite almost surely by the displayed bounds. No unconditional moment is asserted for a start outside . Step 2.1 gives the pathwise Lipschitz estimate for all finite starting states. Time homogeneity makes all constants uniform in .
Depends on
- Underdamped Langevin equation and canonical Gibbs density
- BAOAB underdamped Langevin splitting
- Exact Ornstein–Uhlenbeck momentum transition and covariance
- The Axiom of Choice
- Itô SDE data and strong solution
- Global strong well-posedness for Lipschitz Itô SDEs
- Multidimensional Ito formula for Brownian-driven processes
- Deterministic Ito integrals are Gaussian
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Sources
- Adam Telatovich and Xiantao Li, Strong Convergence Analysis for Numerical Methods for the Langevin Dynamics Model and Higher-order Operator-splitting Schemes, §§2–3 and 5.2 (standard reference, not scraped)
- Jean-Noël Roux, Stéphane Rodts, and Gabriel Stoltz, Introduction à la physique statistique et à la physique quantique, §§5.2.1–5.2.2 (standard reference, not scraped)
- Benedict Leimkuhler and Charles Matthews, Rational Construction of Stochastic Numerical Methods for Molecular Sampling, §2 and Appendix (standard reference, not scraped)