Paris Dauphine University

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.

Automatic Dantzig---Wolfe reformulation of mixed integer programs

Abstract: Dantzig---Wolfe decomposition (or reformulation) is well-known to provide strong dual bounds for specially structured mixed integer programs (MIPs). However, the method is not implemented in any state-of-the-art MIP solver as it is considered to require structural problem knowledge and tailoring to this structure. We provide a computational proof-of-concept that the reformulation can be automated. That is, we perform a rigorous experimental study, which results in identifying a score to estimate the quality of a decomposition: after building a set of potentially good candidates, we exploit such a score to detect which decomposition might be useful for Dantzig---Wolfe reformulation of a MIP. We experiment with general instances from MIPLIB2003 and MIPLIB2010 for which a decomposition method would not be the first choice, and demonstrate that strong dual bounds can be obtained from the automatically reformulated model using column generation. Our findings support the idea that Dantzig---Wolfe reformulation may hold more promise as a general-purpose tool than previously acknowledged by the research community.

A regularity structure for rough volatility

Abstract: A new paradigm has emerged recently in financial modeling: rough (stochastic) volatility. First observed by Gatheral et al. in high-frequency data, subsequently derived within market microstructure models, rough volatility captures parsimoniously key-stylized facts of the entire implied volatility surface, including extreme skews (as observed earlier by Alos et al.) that were thought to be outside the scope of stochastic volatility models. On the mathematical side, Markovianity and, partially, semimartingality are lost. In this paper, we show that Hairer's regularity structures, a major extension of rough path theory, which caused a revolution in the field of stochastic partial differential equations, also provide a new and powerful tool to analyze rough volatility models.

Incidental focal solid liver lesions: diagnostic performance of contrast-enhanced ultrasound and MR imaging.

Abstract: To prospectively assess the diagnostic performance of contrast-enhanced ultrasound (CEUS) and MR imaging in incidental solid focal liver lesions not characterised on ultrasound. Forty-seven patients with 50 lesions underwent MR imaging and CEUS: 24 focal nodular hyperplasias (FNH), 11 adenomas, 10 haemangiomas, 1 focal fatty change and 4 malignant lesions were identified. Two experienced radiologists randomly reviewed contrast-enhanced MR imaging and CEUS data, and provided the most likely diagnosis. Sensitivity (Se), specificity (Sp), likelihood ratios (LR) and kappa value were calculated. A histotype diagnosis was obtained in 66–52% with MR imaging and 52–53% with CEUS, respectively, for both readers. Se, Sp and LR for haemangioma were 100–100, 100–100 and 78–78 with MR imaging and 89–89, 100–100 and 68–70 with CEUS; for FNH with MR imaging they were 88–63, 96–100 and 23–34 and 74–67, 88–96 and 6–17 with CEUS. If the diagnosis of haemangioma was uncertain with CEUS, MR imaging always confirmed the diagnosis. If the diagnosis of FNH was uncertain with either CEUS or MR imaging, the other imaging technique confirmed the diagnosis in approximately half the cases. Both CEUS and MR imaging have a high diagnostic performance in incidental focal liver lesions and are complementary when diagnosis is uncertain.