Institution
Paris Dauphine University
Education•Paris, France•
About: Paris Dauphine University is a education organization based out in Paris, France. It is known for research contribution in the topics: Population & Approximation algorithm. The organization has 1766 authors who have published 6909 publications receiving 162747 citations. The organization is also known as: Paris Dauphine & Dauphine.
Topics: Population, Approximation algorithm, Bounded function, Parameterized complexity, Time complexity
Papers published on a yearly basis
Papers
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05 Sep 2010
TL;DR: This paper presents a new method for 2-D and 3-D shape retrieval based on geodesic signatures, which allows to propose a unifying framework for the compact description of planar shapes and3-D surfaces.
Abstract: This paper presents a new method for 2-D and 3-D shape retrieval based on geodesic signatures. These signatures are high dimensional statistical distributions computed by extracting several features from the set of geodesic distance maps to each point. The resulting high dimensional distributions are matched to perform retrieval using a fast approximate Wasserstein metric. This allows to propose a unifying framework for the compact description of planar shapes and 3-D surfaces.
50 citations
TL;DR: In this article, the authors studied the continuity of the time derivative of the solution to the one-dimensional parabolic obstacle problem with variable coefficients and proved that the solution is continuous for almost every time.
50 citations
TL;DR: An optimization model is proposed that assigns each individual component to the most efficient line feeding mode among three alternatives which are line stocking, kitting and sequencing modes and is applied to a first tier supplier plant in the automotive sector.
50 citations
TL;DR: In this article, the authors investigate the combined effect of DERs and EVs on grid cost recovery and find that the more a tariff structure gives incentives for DER, the less beneficial it is for EVs.
50 citations
Posted Content•
TL;DR: In this article, the authors reexamine the Malliavin weighting functions introduced by Fournie et al. as a new method for efficient and fast computations of the Greeks.
Abstract: This paper reexamines the Malliavin weighting functions introduced by Fournie et al. (1999) as a new method for efficient and fast computations of the Greeks. Reexpressing the weighting function generator in terms of its Skorohod integrand, we show that these weighting functions have to satisfy necessary and sufficient conditions expressed as conditional expectations. We then derive the weighting function with the smallest total variance. This is of particular interest as it bridges the method of Malliavin weights and the one of likelihood ratio, as introduced by Broadie and Glasserman (1996). The likelihood ratio is precisely the weighting function with the smallest total variance. We finally examine when to use the Malliavin method and when to prefer finite difference.
49 citations
Authors
Showing all 1819 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Pierre-Louis Lions | 98 | 283 | 57043 |
| Laurent D. Cohen | 94 | 417 | 42709 |
| Chris Bowler | 87 | 288 | 35399 |
| Christian P. Robert | 75 | 535 | 36864 |
| Albert Cohen | 71 | 368 | 19874 |
| Gabriel Peyré | 65 | 303 | 16403 |
| Kerrie Mengersen | 65 | 737 | 20058 |
| Nader Masmoudi | 62 | 245 | 10507 |
| Roland Glowinski | 61 | 393 | 20599 |
| Jean-Michel Morel | 59 | 302 | 29134 |
| Nizar Touzi | 57 | 224 | 11018 |
| Jérôme Lang | 57 | 277 | 11332 |
| William L. Megginson | 55 | 169 | 18087 |
| Alain Bensoussan | 55 | 417 | 22704 |
| Yves Meyer | 53 | 128 | 14604 |