2010
DOI: 10.1007/s00211-010-0329-0
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A numerical scheme for a class of sweeping processes

Abstract: The aim of this paper is to study a whole class of first order differential inclusions, which fit into the framework of perturbed sweeping process by uniformly prox-regular sets. After obtaining well-posedness results, we propose a numerical scheme based on a prediction-correction algorithm and we prove its convergence. Finally we apply these results to a problem coming from the modelling of crowd motion.

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Cited by 50 publications
(79 citation statements)
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“…2.9) for a result with H = R n under some boundedness assumptions related to the first and second derivatives of the constraints functions g k (t, ·). Our statement, approach and proof of Theorem 9.1 are general and different from those of the aforementioned results in [34,35].…”
Section: This and Proposition 24 Justify The R-prox-regularity Of Thmentioning
confidence: 64%
“…2.9) for a result with H = R n under some boundedness assumptions related to the first and second derivatives of the constraints functions g k (t, ·). Our statement, approach and proof of Theorem 9.1 are general and different from those of the aforementioned results in [34,35].…”
Section: This and Proposition 24 Justify The R-prox-regularity Of Thmentioning
confidence: 64%
“…31,45 The idea is borrowed from granular flow simulations 26 : we denote by q n the position vector at time t n . The next position vector is q n+1 = q n + τ u n+1 , where τ = t n+1 − t n > 0 is the time step.…”
Section: Numerical Aspectsmentioning
confidence: 99%
“…This proof is a modification of a similar proof presented in [38], the first part goes in the same way, yet for the second part we depart from finite-dimensional setting into the infinite-dimensional one and then return back.…”
Section: Remarkmentioning
confidence: 92%
“…This follows from the discretization scheme used in [38]. The main argument is that the points z j will not be far away from the set Z j and thus the projection will retain its single-valued Lipschitzian character, even though the set may be nonconvex.…”
Section: Remarkmentioning
confidence: 99%
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