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: Context (language use) & Population. The organization has 1766 authors who have published 6909 publications receiving 162747 citations. The organization is also known as: Paris Dauphine & Dauphine.

Conjugate points and shocks in nonlinear optimal control

Abstract: In this paper the authors use the method of characteristics to extend the Jacobi conjugate points theory to the Bolza problem arising in nonlinear optimal control. This yields necessary and sufficient optimality conditions for weak and strong local minima stated in terms of the existence of a solution to a corresponding matrix Riccati differential equation. The same approach allows to investigate as well smoothness of the value function.

Manipulating picking sequences

Abstract: Picking sequences are a natural way of allocating indivisible items to agents in a decentralized manner: at each stage, a designated agent chooses an item among those that remain available. We address the computational issues of the manipulation of picking sequences by an agent or a coalition of agents. We show that a single agent can compute an optimal manipulation in polynomial time. Then we consider several notions of coalitional manipulation; for one of these notions, we show that computing an optimal manipulation is easy. We temper these results by giving a nontrivial upper bound on the impact of manipulation on the loss of social welfare.

Sparse regularization on thin grids I: the Lasso

Abstract: This article analyzes the recovery performance in the presence of noise of sparse L1 regularization, which is often referred to as the Lasso or Basis-Pursuit. We study the behavior of the method for inverse problems regularization when the discretization step size tends to zero. We assume that the sought after sparse sum of Diracs is recovered when there is no noise (a condition which has been thoroughly studied in the literature) and we study what is the support (in particular the number of Dirac masses) estimated by the Lasso when noise is added to the observation. We identify a precise non-degeneracy condition that guarantees that the recovered support is close to the initial one. More precisely, we show that, in the small noise regime, when the non-degeneracy condition holds, this method estimates twice the number of spikes as the number of original spikes. Indeed, we prove that the Lasso detects two neighboring spikes around each location of an original spike. While this paper is focussed on cases where the observations vary smoothly with the spikes locations (e.g. the deconvolution problem with a smooth kernel), an interesting by-product is an abstract analysis of the support stability of discrete L1 regularization, which is of an independent interest. We illustrate the usefulness of this abstract analysis to analyze for the first time the support instability of compressed sensing recovery.

Efficient fast marching with Finsler metrics

Abstract: We study the discretization of the escape time problem: find the length of the shortest path joining an arbitrary point $$z$$ z of a domain $$\Omega $$ Ω , to the boundary $$\partial \Omega $$ ? Ω . Path length is measured locally via a Finsler metric, potentially asymmetric and strongly anisotropic. This optimal control problem can be reformulated as a static Hamilton---Jacobi partial differential equation, or as a front propagation model. It has numerous applications, ranging from motion planning to image segmentation. We introduce a new algorithm, fast marching using anisotropic stencil refinement (FM-ASR), which addresses this problem on a two dimensional domain discretized on a cartesian grid. The local stencils used in our discretization are produced by arithmetic means, like in the FM-LBR (Mirebeau in Anisotropic fast Marching on Cartesian grids, using lattice basis reduction, preprint 2012), a method previously introduced by the author in the special case of Riemannian metrics. The complexity of the FM-ASR, in an average sense over all grid orientations, only depends (poly-)logarithmically on the anisotropy ratio of the metric, while most alternative approaches have a polynomial dependence. Numerical experiments show, in several occasions, that the accuracy/complexity compromise is improved by an order of magnitude or more.

Greenhouse gas intensity of three main crops and implications for low-carbon agriculture in China

Abstract: China faces significant challenges in reconciling food security goals with the objective of becoming a low-carbon economy. Agriculture accounts for approximately 11 % of China’s national greenhouse gas (GHG) emissions with cereal production representing a large proportion (about 32 %) of agricultural emissions. Minimizing emissions per unit of product is a policy objective and we estimated the GHG intensities (GHGI) of rice, wheat and maize production in China from 1985 to 2010. Results show significant variations of GHGIs among Chinese provinces and regions. Relative to wheat and maize, GHGI of rice production is much higher owing to CH4 emissions, and is more closely related to yield levels. In general, the south and central has been the most carbon intensive region in rice production while the GHGI of wheat production is highest in north and northwest provinces. The southwest has been characterized by the highest maize GHGI but the lowest rice GHGI. Compared to the baseline scenario, a 2 % annual reduction in N inputs, combined with improved water management in rice paddies, would mitigate 17 % of total GHG emissions from cereal production in 2020 while sustaining the required yield increase to ensure food security. Better management practices will entail additional gains in soil organic carbon further decreasing GHGI. To realize the full mitigation potential while maximizing agriculture development, the design of appropriate policies should accommodate local conditions.