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Scaled RATTLE residuals bound the finite solve trajectory error
Statement
Let be a compact subset of the regular phase manifold, contained in a convex phase-coordinate chart whose closure lies inside a regular chart. Let , let be its exact RATTLE step, and let be a successful output of Finite budget RATTLE solve with scaled constraint residuals whose multiplier iterates remain in the chosen convex branch balls over . Define For sufficiently small , there is a constant uniform over such that For the repeated-trajectory claim, choose compact phase tubes in such a chart, with the coordinate image of contained in a convex coordinate neighborhood with compact closure inside the regular chart. Suppose the exact-solve RATTLE iterates from a common initial state in remain in on the fixed-time grids under consideration. Restore each successful finite-solve output using a local retraction from Holonomic phase residuals control distance to the consistent phase manifold before using it as the next input. If both scaled residuals are bounded by a fixed sufficiently small at each step, then for sufficiently small the restored trajectory remains in and differs from the exact-solve RATTLE trajectory by uniformly on the grid. If instead both residuals are at most , the difference is , and the unscaled position and tangency residuals of each unprojected solver output are respectively and . The finite solver's raw output need not be consistent, so the global estimate is for the restored trajectory.
Facts & Assumptions
Given: A compact phase subset , a consistent input , exact and approximate multiplier iterates in the selected branch neighborhoods over , and the scaled residuals in the Statement.
Scaled classical RATTLE step map: the exact updates use the impulses , , and impose , .
Scaled RATTLE equations have unique local multiplier branches: the scaled equations have unique smooth local roots, with a common step range and uniform derivative and inverse-Jacobian bounds on each compact phase subset.
Finite budget RATTLE solve with scaled constraint residuals: on its success status the algorithm returns the state, both multiplier iterates, and the scaled residual norms defined above.
Implicit residuals and their update and derivative units: a residual norm is not a forward-error bound without a separate inverse or stability estimate.
On a convex open set, a uniform bound implies : a uniform derivative bound on a convex neighborhood yields a uniform Lipschitz estimate.
Holonomic phase residuals control distance to the consistent phase manifold: finitely many local smooth retractions cover a compact regular phase set, and all satisfy the uniform bound on their domains.
Proof
For , [F2] gives uniformly on the branch ball. Shrink it so . The derivative of is then bounded by . Since at the exact root , [F5] along the segment to gives . As , it follows that . This inverse estimate is needed because [F4] alone does not convert residual size to state error.
The position formula gives and . The uniform matrix bounds and step 1.1 yield and .
For fixed , the final scaled tangency residual is affine in with derivative exactly . Uniform invertibility bounds the distance from to the exact momentum root for this by . The smooth momentum branch in [F2] changes that exact root by at most when replaces , using step 1.1 and the uniform branch derivative bound.
In the final momentum formula, the initial half-momentum difference contributes by step 2.1; changing the position in the smooth force and constraint Jacobian contributes ; changing the final multiplier contributes by step 2.2. Their sum proves and hence the one-step estimates.
The residual definitions give and . Whenever the output lies in a neighborhood from [F6], its local retraction changes it by at most for . For the repeated-trajectory claim, choose a compact phase neighborhood of with closure inside the regular chart. The compact branch bounds and imply for sufficiently small . Apply [F6] to and choose its finite retraction cover with a common distance constant. The union of these open domains contains the compact set , hence contains some uniform ambient neighborhood of it. For every restored input in , step 3.1 puts its raw output within of the exact step from that same input in . Bounded scaled residuals and sufficiently small therefore place the raw output in this union. The same-input exact step need not belong to the reference tube . Thus the retraction is defined, and the restored update differs from the exact RATTLE update by at most .
Work in the phase coordinates of the Statement and choose a slightly larger compact convex coordinate neighborhood on which the branches and their first derivatives are bounded by [F2]. Since is the identity, uniformly there, so [F5] gives a Lipschitz factor for small positive . The one-step estimate and the retraction bound, with equivalence of ambient and chart norms on the compact tube, bound each restored solver perturbation by . Starting with the same initial state, the error therefore obeys until a first exit from . Iteration gives for fixed residual bound and when each residual is at most . Choose small enough, or small enough in the second case, that this bound is less than the positive coordinate distance from to the boundary of ; then no first exit occurs. Step 4.1, applied until this first-exit argument closes, places every raw output in a retraction neighborhood for small . Finally, the unscaled residuals are exactly and , hence and in the second case.
Depends on
- Regular holonomic phase space and constrained dynamics
- Scaled classical RATTLE step map
- Scaled RATTLE equations have unique local multiplier branches
- Finite budget RATTLE solve with scaled constraint residuals
- Implicit residuals and their update and derivative units
- Holonomic phase residuals control distance to the consistent phase manifold
- On a convex open set, a uniform bound $\|Df(z)v\|_2\le M\|v\|_2$ implies $\|f(y)-f(x)\|_2\le M\|y-x\|_2$
Used by
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Sources
- Ernst Hairer, Christian Lubich, and Gerhard Wanner, Geometric Numerical Integration, 2nd ed., Chapter VII.1.4, pp. 245–247 (standard reference, not scraped)
- Robert I. McLachlan, Klas Modin, Olivier Verdier, and Matt Wilkins, Geometric Generalisations of SHAKE and RATTLE, Foundations of Computational Mathematics 14 (2014), 339–370, §§4.1 and 4.3 (standard reference, not scraped)