Publications

2017

• Cell Averaging Two-Scale Convergence: Applications to Periodic Homogenization
  
  • Alouges François
  • Di Fratta Giovanni
  Multiscale Modeling and Simulation: A SIAM Interdisciplinary Journal, Society for Industrial and Applied Mathematics, 2017, 15 (4), pp.1651-1671. (10.1137/16M1085309)
  DOI : 10.1137/16M1085309
• A numerical approach to determine mutant invasion fitness and evolutionary singular strategies
  
  • Fritsch Coralie
  • Campillo Fabien
  • Ovaskainen Otso
  Theoretical Population Biology, Elsevier, 2017, 115, pp.89-99. We propose a numerical approach to study the invasion fitness of a mutant and to determine evolutionary singular strategies in evolutionary structured models in which the competitive exclusion principle holds. Our approach is based on a dual representation, which consists of the modelling of the small size mutant population by a stochastic model and the computation of its corresponding deterministic model. The use of the deterministic model greatly facilitates the numerical determination of the feasibility of invasion as well as the convergence-stability of the evolutionary singular strategy. Our approach combines standard adaptive dynamics with the link between the mutant survival criterion in the stochastic model and the sign of the eigenvalue in the corresponding deterministic model. We present our method in the context of a mass-structured individual-based chemostat model. We exploit a previously derived mathematical relationship between stochastic and deterministic representations of the mutant population in the chemostat model to derive a general numerical method for analyzing the invasion fitness in the stochastic models. Our method can be applied to the broad class of evolutionary models for which a link between the stochastic and deterministic invasion fitnesses can be established. (10.1016/j.tpb.2017.05.001)
  DOI : 10.1016/j.tpb.2017.05.001
• Sampling method for sign changing contrast
  
  • Audibert Lorenzo
  Inverse Problems and Imaging, AIMS American Institute of Mathematical Sciences, 2017. We extend the applicability of the Generalized Linear Sampling Method (GLSM) [2] and the Factorization Method (FM)[14] to the case of inhomogeneities where the contrast change sign strictly inside the obstacle. Both methods give an exact characterization of the target shapes in term of the fareld operator (at a xed frequency). One of the key ingredient to prove this exact characterization is based on a factorization of the fareld operator. This factorization involves three operators which should exhibit specic properties. This paper is concerned with the extension of the coercivity property required on one of them to the case of sign changing contrast both for isotropic and anisotropic scatters with possibly dierent supports for the isotropic and anisotropic parts. We fnally validate the method through some numerical tests in two dimensions.
• Parameter Estimation in Nonlinear Mixed Effect Models Using saemix, an R Implementation of the SAEM Algorithm
  
  • Comets Emmanuelle
  • Lavenu Audrey Paris
  • Lavielle Marc
  Journal of Statistical Software, University of California, Los Angeles, 2017, 80 (3), pp.i03. The saemix package for R provides maximum likelihood estimates of parameters in nonlinear mixed effect models, using a modern and efficient estimation algorithm, the stochastic approximation expectation-maximisation (SAEM) algorithm. In the present paper we describe the main features of the package, and apply it to several examples to illustrate its use. Making use of S4 classes and methods to provide user-friendly interaction, this package provides a new estimation tool to the R community. (10.18637/jss.v080.i03)
  DOI : 10.18637/jss.v080.i03
• An approximation of the M 2 closure: application to radiotherapy dose simulation
  
  • Pichard T
  • Alldredge G W
  • Brull Stéphane
  • Dubroca B
  • Frank M
  Journal of Scientific Computing, Springer Verlag, 2017. Particle transport in radiation therapy can be modelled by a kinetic equation which must be solved numerically. Unfortunately, the numerical solution of such equations is generally too expensive for applications in medical centers. Moment methods provide a hierarchy of models used to reduce the numerical cost of these simulations while preserving basic properties of the solutions. Moment models require a closure because they have more unknowns than equations. The entropy-based closure is based on the physical description of the particle interactions and provides desirable properties. However, computing this closure is expensive. We propose an approximation of the closure for the first two models in the hierarchy, the M 1 and M 2 models valid in one, two or three dimensions of space. Compared to other approximate closures, our method works in multiple dimensions. We obtain the approximation by a careful study of the domain of realizability and by invariance properties of the entropy minimizer. The M 2 model is shown to provide significantly better accuracy than the M 1 model for the numerical simulation of a dose computation in radiotherapy. We propose a numerical solver using those approximated closures. Numerical experiments in dose computation test cases show that the new method is more efficient compared to numerical solution of the minimum entropy problem using standard software tools.
• The infinitesimal model: definition, derivation, and implications
  
  • Barton Nick
  • Etheridge Alison M
  • Véber Amandine
  Theoretical Population Biology, Elsevier, 2017. Our focus here is on the infinitesimal model. In this model, one or several quantitative traits are described as the sum of a genetic and a non-genetic component, the first being distributed within families as a normal random variable centred at the average of the parental genetic components, and with a variance independent of the parental traits. Thus, the variance that segregates within families is not perturbed by selection, and can be predicted from the variance components. This does not necessarily imply that the trait distribution across the whole population should be Gaussian, and indeed selection or population structure may have a substantial effect on the overall trait distribution. One of our main aims is to identify some general conditions on the allelic effects for the infinitesimal model to be accurate. We first review the long history of the infinitesimal model in quantitative genetics. Then we formulate the model at the phenotypic level in terms of individual trait values and relationships between individuals, but including different evolutionary processes: genetic drift, recombination, selection, mutation , population structure, ... We give a range of examples of its application to evolutionary questions related to stabilising selection, assortative mating, effective population size and response to selection, habitat preference and speciation. We provide a mathematical justification of the model as the limit as the number M of underlying loci tends to infinity of a model with Mendelian inheritance, mutation and environmental noise, when the genetic component of the trait is purely additive. We also show how the model generalises to include epistatic effects. We prove in particular that, within each family, the genetic components of the individual trait values in the current generation are indeed normally distributed with a variance independent of ancestral traits, up to an error of order 1/\sqrt{M}. Simulations suggest that in some cases the convergence may be as fast as 1/\sqrt{M} .
• HOMOGENIZATION OF STOKES SYSTEM USING BLOCH WAVES
  
  • Allaire Grégoire
  • Ghosh Tuhin
  • Vanninathan Muthusamy
  Networks and Heterogeneous Media, American Institute of Mathematical Sciences, 2017, 12 (4), pp.525-550. In this work, we study the Bloch wave homogenization for the Stokes system with periodic viscosity coefficient. In particular, we obtain the spectral interpretation of the homogenized tensor. The presence of the incompressibility constraint in the model raises new issues linking the homogenized tensor and the Bloch spectral data. The main difficulty is a lack of smoothness for the bottom of the Bloch spectrum, a phenomenon which is not present in the case of the elasticity system. This issue is solved in the present work, completing the homogenization process of the Stokes system via the Bloch wave method.
• Adaptive multipreconditioned FETI: scalability results and robustness assessment
  
  • Bovet Christophe
  • Parret-Fréaud Augustin
  • Spillane Nicole
  • Gosselet Pierre
  Computers & Structures, Elsevier, 2017, 193, pp.1-20. The purpose of this article is to assess the adaptive multipreconditioned FETI solvers (AMPFETI) on realistic industrial problems and hardware. The multi-preconditioned FETI algorithm (first introduced as Simultaneous FETI [1]) is a non-overlapping domain decomposition method which exhibits good robust-ness properties without requiring the explicit knowledge of the original partial differential equation, or any a priori analysis of the algebraic system through eigenvalues problems. Multipreconditioned FETI solves critical problems in significantly fewer iterations than classical FETI but each iteration involves a larger computational effort. An adaptive strategy (known as the adaptive mul-tipreconditioned conjugate gradient algorithm [2]) has been proposed to achieve balance between robustness and efficiency and we will observe that it provides an efficient solver for the problems considered here. (10.1016/j.compstruc.2017.07.010)
  DOI : 10.1016/j.compstruc.2017.07.010
• Understanding the Time-Dependent Effective Diffusion Coefficient Measured by Diffusion MRI: the Intra-Cellular Case
  
  • Haddar Houssem
  • Li Jing-Rebecca
  • Schiavi Simona
  SIAM Journal on Applied Mathematics, Society for Industrial and Applied Mathematics, 2017. Diffusion Magnetic Resonance Imaging (dMRI) can be used to measure a time-dependent effective diffusion coefficient that can in turn reveal information about the tissue geometry. Recently a mathematical model for the time-dependent effective diffusion coefficient was obtained using homogenization techniques after imposing a certain scaling relationship for the time, the biological cell membrane permeability, the diffusion-encoding magnetic field gradient strength, and a periodicity length of the cellular geometry. With this choice of the scaling of the physical parameters, the effective diffusion coefficient of the medium can be computed after solving a diffusion equation subject to a time-dependent Neumann boundary condition, independently in the biological cells and in the extra-cellular space. In this paper, we analyze this new model, which we call the H-ADC model, in the case of finite domains, which is relevant to diffusion inside biological cells. We use both the eigenfunction expansion and the single layer potential representation for the solution of the above mentioned diffusion equation to obtain analytical expressions for the effective diffusion coefficient in different diffusion time regimes. These expressions are validated using numerical simulations in two dimensions.
• On perturbed proximal gradient algorithms
  
  • Atchadé Yves
  • Fort Gersende
  • Moulines Éric
  Journal of Machine Learning Research, Microtome Publishing, 2017, 18 (10), pp.1-33.
• Online Learning and Blackwell Approachability with Partial Monitoring: Optimal Convergence Rates
  
  • Kwon Joon
  • Perchet Vianney
  JMLR Papers, 2017, 54, pp.604-613. Blackwell approachability is an online learning setup generalizing the classical problem of regret minimization by allowing for instance multi-criteria optimization, global (online) optimization of a convex loss, or online linear optimization under some cumulative constraint. We consider partial monitoring where the decision maker does not necessarily observe the outcomes of his decision (unlike the traditional regret/bandit literature). Instead, he receives a random signal correlated to the decision-outcome pair, or only to the outcome. We construct, for the first time, approachability algorithms with convergence rate of order O(T −1/2) when the signal is independent of the decision and of order O(T −1/3) in the case of general signals. Those rates are optimal in the sense that they cannot be improved without further assumption on the structure of the objectives and/or the signals.
• Équations de Navier-Stokes incompressibles et multirésolution spatiale adaptative: sur la question des modes parasites en maillage collocalisé.
  
  • N'Guessan Marc-Arthur
  • Massot Marc
  • Tenaud Christian
  • Series Laurent
  , 2017. La simulation numérique directe (DNS) de la combustion avec chimie détaillée et transport multi-espèces représente l'un des défis les plus importants en matière de calcul scientifique dans de nombreuses applications industrielles. Un des enjeux est de coupler un solveur hydrodynamique pour la résolution des équations de Navier-Stokes, pour un mélange réactif dans la limite des faibles nombres de Mach, à une stratégie de résolution de systèmes de convection-réaction-diffusion, tout en maintenant l'efficacité algorithmique, l'adaptation temps-espace et le contrôle d'erreur. La présente communication vise à proposer une stratégie optimale pour l'élimination des modes parasites dans un contexte de maillages colocalisés en volume fini, dans un cadre de multirésolution spatiale.
• Generalized and hybrid Metropolis-Hastings overdamped Langevin algorithms
  
  • Poncet Romain
  , 2017. It has been shown that the nonreversible overdamped Langevin dynamics enjoy better convergence properties in terms of spectral gap and asymptotic variance than the reversible one. In this article we propose a variance reduction method for the Metropolis-Hastings Adjusted Langevin Algorithm (MALA) that makes use of the good behaviour of the these nonreversible dynamics. It consists in constructing a nonreversible Markov chain (with respect to the target invariant measure) by using a Generalized Metropolis-Hastings adjustment on a lifted state space. We present two variations of this method and we discuss the importance of a well-chosen proposal distribution in terms of average rejection probability. We conclude with numerical experimentations to compare our algorithms with the MALA, and show variance reduction of several orders of magnitude in some favourable toy cases.
• Exploring the complexity of the integer image problem in the max-algebra
  
  • Maccaig Marie
  Discrete Applied Mathematics, Elsevier, 2017, 217 (2), pp.261--275. We investigate the complexity of the problem of finding an integer vector in the max-algebraic column span of a matrix, which we call the integer image problem. We show some cases where we can determine in strongly polynomial time whether such an integer vector exists, and find such an integer vector if it does exist. On the other hand we also describe a group of related problems each of which we prove to be NP-hard. Our main results demonstrate that the integer image problem is equivalent to finding a special type of integer image of a matrix satisfying a property we call column typical. For a subclass of matrices this problem is polynomially solvable but if we remove the column typical assumption then it becomes NP-hard. (10.1016/j.dam.2016.09.016)
  DOI : 10.1016/j.dam.2016.09.016
• Quantitative DLA-based compressed sensing for T1-weighted acquisitions.
  
  • Svehla Pavel
  • Nguyen Khieu-Van
  • Li Jing-Rebecca
  • Ciobanu Luisa
  Journal of Magnetic Resonance, Elsevier, 2017. (10.1016/j.jmr.2017.05.002)
  DOI : 10.1016/j.jmr.2017.05.002
• Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles
  
  • Ollivier Yann
  • Arnold Ludovic
  • Auger Anne
  • Hansen Nikolaus
  Journal of Machine Learning Research, Microtome Publishing, 2017, 18 (18), pp.1-65. We present a canonical way to turn any smooth parametric family of probability distributions on an arbitrary search space X into a continuous-time black-box optimization method on X, the information-geometric optimization (IGO) method. Invariance as a major design principle keeps the number of arbitrary choices to a minimum. The resulting IGO flow is the flow of an ordinary differential equation conducting the natural gradient ascent of an adaptive, time-dependent transformation of the objective function. It makes no particular assumptions on the objective function to be optimized. The IGO method produces explicit IGO algorithms through time discretization. It naturally recovers versions of known algorithms and offers a systematic way to derive new ones. In continuous search spaces, IGO algorithms take a form related to natural evolution strategies (NES). The cross-entropy method is recovered in a particular case with a large time step, and can be extended into a smoothed, parametrization-independent maximum likelihood update (IGO-ML). When applied to the family of Gaussian distributions on R^d, the IGO framework recovers a version of the well-known CMA-ES algorithm and of xNES. For the family of Bernoulli distributions on {0, 1}^d, we recover the seminal PBIL algorithm and cGA. For the distributions of restricted Boltzmann machines, we naturally obtain a novel algorithm for discrete optimization on {0, 1}^d. All these algorithms are natural instances of, and unified under, the single information-geometric optimization framework. The IGO method achieves, thanks to its intrinsic formulation, maximal invariance properties: invariance under reparametrization of the search space X, under a change of parameters of the probability distribution, and under increasing transformation of the function to be optimized. The latter is achieved through an adaptive, quantile-based formulation of the objective. Theoretical considerations strongly suggest that IGO algorithms are essentially characterized by a minimal change of the distribution over time. Therefore they have minimal loss in diversity through the course of optimization, provided the initial diversity is high. First experiments using restricted Boltzmann machines confirm this insight. As a simple consequence, IGO seems to provide, from information theory, an elegant way to simultaneously explore several valleys of a fitness landscape in a single run.
• Resolution Analysis of Passive Synthetic Aperture Imaging of Fast Moving Objects
  
  • Garnier Josselin
  • Borcea L.
  • Papanicolaou G.
  • Solna K.
  • Tsogka C.
  SIAM Journal on Imaging Sciences, Society for Industrial and Applied Mathematics, 2017, 10 (2), pp.665 - 710. (10.1137/16M109716X)
  DOI : 10.1137/16M109716X
• Functional Characterization of Intrinsic and Extrinsic Geometry
  
  • Corman Etienne
  • Solomon Justin
  • Ben-Chen Mirela
  • Guibas Leonidas
  • Ovsjanikov Maks
  ACM Transactions on Graphics, Association for Computing Machinery, 2017, 17. We propose a novel way to capture and characterize distortion between pairs of shapes by extending the recently proposed framework of shape differences built on functional maps. We modify the original definition of shape differences slightly and prove that, after this change, the discrete metric is fully encoded in two shape difference operators and can be recovered by solving two linear systems of equations. Then, we introduce an extension of the shape difference operators using offset surfaces to capture extrinsic or embedding-dependent distortion, complementing the purely intrinsic nature of the original shape differences. Finally, we demonstrate that a set of four operators is complete, capturing intrinsic and extrinsic structure and fully encoding a shape up to rigid motion in both discrete and continuous settings. We highlight the usefulness of our constructions by showing the complementary nature of our extrinsic shape differences in capturing distortion ignored by previous approaches. We additionally provide examples where we recover local shape structure from the shape difference operators, suggesting shape editing and analysis tools based on manipulating shape differences.
• An equilibrated fluxes approach to the certified descent algorithm for shape optimization using conforming finite element and discontinuous Galerkin discretizations
  
  • Giacomini Matteo
  Journal of Scientific Computing, Springer Verlag, 2017. The certified descent algorithm (CDA) is a gradient-based method for shape optimization which certifies that the direction computed using the shape gradient is a genuine descent direction for the objective functional under analysis. It relies on the computation of an upper bound of the error introduced by the finite element approximation of the shape gradient. In this paper, we present a goal-oriented error estimator which depends solely on local quantities and is fully-computable. By means of the equilibrated fluxes approach, we construct a unified strategy valid for both conforming finite element approximations and discontinuous Galerkin discretizations. The new variant of the CDA is tested on the inverse identification problem of electrical impedance tomography: both its ability to identify a genuine descent direction at each iteration and its reliable stopping criterion are confirmed. (10.1007/s10915-017-0545-1)
  DOI : 10.1007/s10915-017-0545-1
• Dependence of tropical eigenspaces
  
  • Niv Adi
  • Rowen Louis
  Communications in Algebra, Taylor & Francis, 2017, 45 (3), pp.924-942. We study the pathology that causes tropical eigenspaces of distinct su-pertropical eigenvalues of a non-singular matrix A, to be dependent. We show that in lower dimensions the eigenvectors of distinct eigenvalues are independent, as desired. The index set that differentiates between subsequent essential monomials of the characteristic polynomial, yields an eigenvalue λ, and corresponds to the columns of adj(A + λI) from which the eigenvectors are taken. We ascertain the cause for failure in higher dimensions, and prove that independence of the eigenvectors is recovered in case the " difference criterion " holds, defined in terms of disjoint differences between index sets of subsequent coefficients. We conclude by considering the eigenvectors of the matrix A ∇ := 1 det(A) adj(A) and the connection of the independence question to generalized eigenvectors. (10.1080/00927872.2016.1172603)
  DOI : 10.1080/00927872.2016.1172603
• A characterization of switched linear control systems with finite L 2 -gain
  
  • Chitour Yacine
  • Mason Paolo
  • Sigalotti Mario
  IEEE Transactions on Automatic Control, Institute of Electrical and Electronics Engineers, 2017, 62, pp.1825-1837. Motivated by an open problem posed by J.P. Hespanha, we extend the notion of Barabanov norm and extremal trajectory to classes of switching signals that are not closed under concatenation. We use these tools to prove that the finiteness of the L2-gain is equivalent, for a large set of switched linear control systems, to the condition that the generalized spectral radius associated with any minimal realization of the original switched system is smaller than one. (10.1109/tac.2016.2593678)
  DOI : 10.1109/tac.2016.2593678
• Log-majorization of the moduli of the eigenvalues of a matrix polynomial by tropical roots
  
  • Akian Marianne
  • Gaubert Stéphane
  • Sharify Meisam
  Linear Algebra and its Applications, Elsevier, 2017, 528, pp.394--435. We show that the sequence of moduli of the eigenvalues of a matrix polynomial is log-majorized, up to universal constants, by a sequence of "tropical roots" depending only on the norms of the matrix coefficients. These tropical roots are the non-differentiability points of an auxiliary tropical polynomial, or equivalently, the opposites of the slopes of its Newton polygon. This extends to the case of matrix polynomials some bounds obtained by Hadamard, Ostrowski and Pólya for the roots of scalar polynomials. We also obtain new bounds in the scalar case, which are accurate for "fewnomials" or when the tropical roots are well separated. (10.1016/j.laa.2016.11.004)
  DOI : 10.1016/j.laa.2016.11.004
• Matched-Filter and Correlation-Based Imaging for Fast Moving Objects Using a Sparse Network of Receivers
  
  • Garnier Josselin
  • Fournier J.
  • Papanicolaou G.
  • Tsogka C.
  SIAM Journal on Imaging Sciences, Society for Industrial and Applied Mathematics, 2017, 10 (4), pp.2165 - 2216. (10.1137/17M112364X)
  DOI : 10.1137/17M112364X
• Certified Descent Algorithm for shape optimization driven by fully-computable a posteriori error estimators
  
  • Giacomini Matteo
  • Pantz Olivier
  • Trabelsi Karim
  ESAIM: Control, Optimisation and Calculus of Variations, EDP Sciences, 2017, 23 (3), pp.977-1001. In this paper we introduce a novel certified shape optimization strategy-named Certified Descent Algorithm (CDA)-to account for the numerical error introduced by the Finite Element approximation of the shape gradient. We present a goal-oriented procedure to derive a certified upper bound of the error in the shape gradient and we construct a fully-computable, constant-free a posteriori error estimator inspired by the complementary energy principle. The resulting CDA is able to identify a genuine descent direction at each iteration and features a reliable stopping criterion. After validating the error estimator, some numerical simulations of the resulting certified shape optimization strategy are presented for the well-known inverse identification problem of Electrical Impedance Tomography. (10.1051/cocv/2016021)
  DOI : 10.1051/cocv/2016021
• Optimal scaling of the Random Walk Metropolis algorithm under Lp mean differentiability
  
  • Durmus Alain
  • Le Corff Sylvain
  • Moulines Éric
  • Roberts Gareth O. O.
  Journal of Applied Probability, Cambridge University press, 2017, 54 (4), pp.1233 -1260. This paper considers the optimal scaling problem for high-dimensional random walk Metropolis algorithms for densities which are differentiable in Lp mean but which may be irregular at some points (like the Laplace density for example) and/or are supported on an interval. Our main result is the weak convergence of the Markov chain (appropriately rescaled in time and space) to a Langevin diffusion process as the dimension d goes to infinity. Because the log-density might be non-differentiable, the limiting diffusion could be singular. The scaling limit is established under assumptions which are much weaker than the one used in the original derivation of [6]. This result has important practical implications for the use of random walk Metropolis algorithms in Bayesian frameworks based on sparsity inducing priors. (10.1017/jpr.2017.61)
  DOI : 10.1017/jpr.2017.61