Publications

2023

• Unbiased constrained sampling with Self-Concordant Barrier Hamiltonian Monte Carlo
  
  • Noble Maxence
  • de Bortoli Valentin
  • Durmus Alain
  , 2022.
• Design patterns of hierarchies for order structures
  
  • Allamigeon Xavier
  • Canu Quentin
  • Cohen Cyril
  • Sakaguchi Kazuhiko
  • Strub Pierre-Yves
  , 2023. Using order structures in a proof assistant naturally raises the problem of working with multiple instances of a same structure over a common type of elements. This goes against the main design pattern of hierarchies used for instance in Coq's MathComp or Lean's mathlib libraries, where types are canonically associated to at most one instance and instances share a common overloaded syntax. We present new design patterns to leverage these issues, and apply them to the formalization of order structures in the MathComp library. A common idea in these patterns is underloading, i.e., a disambiguation of operators on a common type. In addition, our design patterns include a way to deal with duality in order structures in a convenient way. We hence formalize a large hierarchy which includes partial orders, semilattices, lattices as well as many variants. We finally pay a special attention to order substructures. We introduce a new kind of structure called prelattice. They are abstractions of semilattices, and allow us to deal with finite lattices and their sublattices within a common signature. As an application, we report on significant simplifications of the formalization of the face lattices of polyhedra in the Coq-Polyhedra library.
• Non-asymptotic convergence bounds for Sinkhorn iterates and their gradients: a coupling approach
  
  • Greco Giacomo
  • Noble Maxence
  • Conforti Giovanni
  • Durmus Alain
  , 2023. Computational optimal transport (OT) has recently emerged as a powerful framework with applications in various fields. In this paper we focus on a relaxation of the original OT problem, the entropic OT problem, which allows to implement efficient and practical algorithmic solutions, even in high dimensional settings. This formulation, also known as the Schrödinger Bridge problem, notably connects with Stochastic Optimal Control (SOC) and can be solved with the popular Sinkhorn algorithm. In the case of discrete-state spaces, this algorithm is known to have exponential convergence; however, achieving a similar rate of convergence in a more general setting is still an active area of research. In this work, we analyze the convergence of the Sinkhorn algorithm for probability measures defined on the d-dimensional torus , that admit densities with respect to the Haar measure. In particular, we prove pointwise exponential convergence of Sinkhorn iterates and their gradient. Our proof relies on the connection between these iterates and the evolution along the Hamilton-Jacobi-Bellman equations of value functions obtained from SOC-problems. Our approach is novel in that it is purely probabilistic and relies on coupling by reflection techniques for controlled diffusions on the torus.
• Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters
  
  • Noble Maxence
  • de Bortoli Valentin
  • Doucet Arnaud
  • Durmus Alain
  , 2023. Multi-marginal Optimal Transport (mOT), a generalization of OT, aims at minimizing the integral of a cost function with respect to a distribution with some prescribed marginals. In this paper, we consider an entropic version of mOT with a tree-structured quadratic cost, i.e., a function that can be written as a sum of pairwise cost functions between the nodes of a tree. To address this problem, we develop Tree-based Diffusion Schrödinger Bridge (TreeDSB), an extension of the Diffusion Schrödinger Bridge (DSB) algorithm. TreeDSB corresponds to a dynamic and continuous state-space counterpart of the multi-marginal Sinkhorn algorithm. A notable use case of our methodology is to compute Wasserstein barycenters which can be recast as the solution of a mOT problem on a star-shaped tree. We demonstrate that our methodology can be applied in high-dimensional settings such as image interpolation and Bayesian fusion.
• Error estimates for finite differences approximations of the total variation
  
  • Caillaud Corentin
  • Chambolle Antonin
  IMA Journal of Numerical Analysis, Oxford University Press (OUP), 2023, 43 (2), pp.692--736. We present a convergence rate analysis of the Rudin-Osher-Fatemi (ROF) denoising problem for two different discretizations of the total variation. The first discretization is the well-known isotropic total variation that suffers from a blurring effect in a special diagonal direction. We prove that in the setting corresponding to this direction, the discrete ROF energy converges to the continuous one in O(h^2/3). The second total variation is based on Raviart-Thomas fields and achieves a O(h) convergence rate for the same quantity under some standard hypotheses. (10.1093/imanum/drac001)
  DOI : 10.1093/imanum/drac001
• A Robust Low-Voltage-Ride-Through Strategy for Grid-Forming Converters Based on Reactive Power Synchronization
  
  • Deng Han
  • Qi Yang
  • Fang Jingyang
  • Tang Yi
  • Debusschere Vincent
  IEEE Transactions on Power Electronics, Institute of Electrical and Electronics Engineers, 2023, 38 (1), pp.346--357. (10.1109/TPEL.2022.3204912)
  DOI : 10.1109/TPEL.2022.3204912
• Time reversal of spinal processes for linear and non-linear branching processes near stationarity
  
  • Henry Benoît
  • Méléard Sylvie
  • Chi Tran Viet
  Electronic Journal of Probability, Institute of Mathematical Statistics (IMS), 2023, 28 (none). We consider a stochastic individual-based population model with competition, trait-structure affecting reproduction and survival, and changing environment. The changes of traits are described by jump processes, and the dynamics can be approximated in large population by a non-linear PDE with a non-local mutation operator. Using the fact that this PDE admits a non-trivial stationary solution, we can approximate the non-linear stochastic population process by a linear birth-death process where the interactions are frozen, as long as the population remains close to this equilibrium. This allows us to derive, when the population is large, the equation satisfied by the ancestral lineage of an individual uniformly sampled at a fixed time $T$, which is the path constituted of the traits of the ancestors of this individual in past times $t\leq T$. This process is a time inhomogeneous Markov process, but we show that the time reversal of this process possesses a very simple structure (e.g. time-homogeneous and independent of $T$). This extends recent results where the authors studied a similar model with a Laplacian operator but where the methods essentially relied on the Gaussian nature of the mutations. (10.1214/23-EJP911)
  DOI : 10.1214/23-EJP911
• The nonlocal isoperimetric problem for polygons: Hardy-Littlewood and Riesz inequalities
  
  • Bogosel Beniamin
  • Bucur Dorin
  • Fragalà Ilaria
  Mathematische Annalen, Springer Verlag, 2023. Given a non-increasing and radially symmetric kernel in $L ^ 1 _{\rm loc} (\Bbb{R} ^ 2 ; \Bbb{R} _+)$, we investigate counterparts of the classical Hardy-Littlewood and Riesz inequalities when the class of admissible domains is the family of polygons with given area and $N$ sides. The latter corresponds to study the polygonal isoperimetric problem in nonlocal version. We prove that, for every $N \geq 3$, the regular $N$-gon is optimal for Hardy-Littlewood inequality. Things go differently for Riesz inequality: while for $N = 3$ and $N = 4$ it is known that the regular triangle and the square are optimal, for $N\geq 5$ we prove that symmetry or symmetry breaking may occur (i.e.\ the regular $N$-gon may be optimal or not), depending on the value of $N$ and on the choice of the kernel. (10.1007/s00208-023-02683-x)
  DOI : 10.1007/s00208-023-02683-x
• Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning
  
  • Dieuleveut Aymeric
  • Fort Gersende
  • Moulines Eric
  • Wai Hoi-To
  IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2023, 71, pp.3117-3148. Stochastic approximation (SA) is a classical algorithm that has had since the early days a huge impact on signal processing, and nowadays on machine learning, due to the necessity to deal with a large amount of data observed with uncertainties. An exemplar special case of SA pertains to the popular stochastic (sub)gradient algorithm which is the working horse behind many important applications. A lesser-known fact is that the SA scheme also extends to non-stochastic-gradient algorithms such as compressed stochastic gradient, stochastic expectation-maximization, and a number of reinforcement learning algorithms. The aim of this article is to overview and introduce the non-stochastic-gradient perspectives of SA to the signal processing and machine learning audiences through presenting a design guideline of SA algorithms backed by theories. Our central theme is to propose a general framework that unifies existing theories of SA, including its non-asymptotic and asymptotic convergence results, and demonstrate their applications on popular non-stochastic-gradient algorithms. We build our analysis framework based on classes of Lyapunov functions that satisfy a variety of mild conditions. We draw connections between non-stochastic-gradient algorithms and scenarios when the Lyapunov function is smooth, convex, or strongly convex. Using the said framework, we illustrate the convergence properties of the non-stochastic-gradient algorithms using concrete examples. Extensions to the emerging variance reduction techniques for improved sample complexity will also be discussed. (10.1109/TSP.2023.3301121)
  DOI : 10.1109/TSP.2023.3301121
• An optimal control-based numerical method for scalar transmission problems with sign-changing coefficients
  
  • Ciarlet Patrick
  • Lassounon David
  • Rihani Mahran
  SIAM Journal on Numerical Analysis, Society for Industrial and Applied Mathematics, 2023, 61 (3), pp.1316-1339. In this work, we present a new numerical method for solving the scalar transmission problem with sign-changing coefficients. In electromagnetism, such a transmission problem can occur if the domain of interest is made of a classical dielectric material and a metal or a metamaterial, with for instance an electric permittivity that is strictly negative in the metal or metamaterial. The method is based on an optimal control reformulation of the problem. Contrary to other existing approaches, the convergence of this method is proved without any restrictive condition. In particular, no condition is imposed on the a priori regularity of the solution to the problem, and no condition is imposed on the meshes, other than that they fit with the interface between the two media. Our results are illustrated by some (2D) numerical experiments. (10.1137/22M1495998)
  DOI : 10.1137/22M1495998
• Particle approximation of the doubly parabolic Keller-Segel equation in the plane
  
  • Fournier Nicolas
  • Tomašević Milica
  Journal of Functional Analysis, Elsevier, 2023, 285 (7), pp.110064. In this work, we study a stochastic system of N particles associated with the parabolicparabolic Keller-Segel system. This particle system is singular and non Markovian in that its drift term depends on the past of the particles. When the sensitivity parameter is sufficiently small, we show that this particle system indeed exists for any N ≥ 2, we show tightness in N of its empirical measure, and that any weak limit point of this empirical measure, as N → ∞, solves some nonlinear martingale problem, which in particular implies that its family of time-marginals solves the parabolic-parabolic Keller-Segel system in some weak sense. The main argument of the proof consists of a Markovianization of the interaction kernel: We show that, in some loose sense, the two-by-two path-dependant interaction can be controlled by a two-by-two Coulomb interaction, as in the parabolic-elliptic case. (10.1016/j.jfa.2023.110064)
  DOI : 10.1016/j.jfa.2023.110064
• Phase recovery from phaseless scattering data for discrete Schrödinger operators
  
  • Novikov Roman
  • Sharma Basant Lal
  Inverse Problems, IOP Publishing, 2023, 39 (12), pp.125006. We consider scattering for the discrete Schrödinger operator on the square lattice Z^d, d ≥ 1, with compactly supported potential. We give formulas for finding the phased scattering amplitude from phaseless near-field scattering data. (10.1088/1361-6420/ad03fe)
  DOI : 10.1088/1361-6420/ad03fe
• Foundations of Modern Statistics
  
  • Belomestny Denis
  • Butucea Cristina
  • Mammen Enno
  • Moulines Eric
  • Reiß Markus
  • Ulyanov Vladimir
  , 2023, 425. (10.1007/978-3-031-30114-8)
  DOI : 10.1007/978-3-031-30114-8
• Counter-Examples in First-Order Optimization: A Constructive Approach
  
  • Goujaud Baptiste
  • Dieuleveut Aymeric
  • Taylor Adrien
  IEEE Control Systems Letters, IEEE, 2023, 7, pp.2485-2490. While many approaches were developed for obtaining worst-case complexity bounds for first-order optimization methods in the last years, there remain theoretical gaps in cases where no such bound can be found. In such cases, it is often unclear whether no such bound exists (e.g., because the algorithm might fail to systematically converge) or simply if the current techniques do not allow finding them. In this work, we propose an approach to automate the search for cyclic trajectories generated by first-order methods. This provides a constructive approach to show that no appropriate complexity bound exists, thereby complementing approaches providing sufficient conditions for convergence. Using this tool, we provide ranges of parameters for which the famous Polyak heavy-ball, Nesterov accelerated gradient, inexact gradient descent, and three-operator splitting algorithms fail to systematically converge, and show that it nicely complements existing tools searching for Lyapunov functions. (10.1109/LCSYS.2023.3286277)
  DOI : 10.1109/LCSYS.2023.3286277
• Graph classes with few $P_4$'s: Universality and Brownian graphon limits
  
  • Lenoir Théo
  , 2023. We consider large uniform labeled random graphs in different classes with few induced $P_4$ ($P_4$ is the graph consisting of a single line of $4$ vertices) which generalize the case of cographs. Our main result is the convergence to a Brownian limit object in the space of graphons. As a by-product we obtain new asymptotic enumerative results for all these graph classes. We also obtain typical density results for a wide variety of induced subgraphs. These asymptotics hold at a smaller scale than what is observable through the graphon convergence. Our proofs rely on tree encoding of graphs. We then use mainly combinatorial arguments, including the symbolic method and singularity analysis.
• A discontinuous Galerkin spectral element method for a nonconservative compressible multicomponent flow model
  
  • Abgrall Rémi
  • Rai Pratik
  • Renac Florent
  Journal of Computational Physics, Elsevier, 2023, 472, pp.111693. In this work, we propose an accurate, robust (the solution remains in the set of states), and stable discretization of a nonconservative model for the simulation of compressible multicomponent flows with shocks and material interfaces. We consider the gamma-based model by Shyue [J. Comput. Phys., 142 (1998), 208-242] where each component follows a stiffened gas equation of state (EOS). We here extend the framework proposed in Renac [J. Comput. Phys., 382 (2019), 1-26] and Coquel et al. [J. Comput. Phys. 431 (2021), 110135] for the discretization of hyperbolic systems, with both fluxes and nonconservative products, to unstructured meshes with curved elements in multiple space dimensions. The framework relies on a high-order discontinuous Galerkin spectral element method (DGSEM) using collocation of quadrature and interpolation points as proposed by Gassner [SIAM J. Sci. Comput., 35 (2013)] in the case of hyperbolic conservation laws. We modify the integrals over discretization elements where we replace the physical fluxes and nonconservative products by two-point numerical fluctuations. The contributions of this work are threefold. First, we analyze the semi-discrete DGSEM discretization of general hyperbolic systems with conservative and nonconservative terms and derive the conditions to obtain a scheme that is high-order accurate, freestream preserving, and entropy stable when excluding material interfaces. Second, we design a three-point scheme with a HLLC solver for the gamma-based model that does not require a root-finding algorithm for the approximation of the nonconservative products. The scheme is proved to be robust and entropy stable for convex entropies, to preserve uniform profiles of pressure and velocity across material interfaces (material interface preservation), and to satisfy a discrete minimum principle on the specific entropy and maximum principles on the parameters of the EOS. Third, the HLLC solver is applied at interfaces in the DGSEM scheme, while we consider two kinds of fluctuations in the integrals over discretization elements: the former is entropy conservative (EC), while the latter preserves material interfaces (CP). Time integration is performed using high-order strong-stability preserving Runge-Kutta schemes. The fully discrete scheme is shown to preserve material interfaces with CP fluctuations. Under a given condition on the time step, both EC and CP fluctuations ensure that the cell-averaged solution remains in the set of states; satisfy a minimum principle on any convex entropy and maximum principles on the EOS parameters. These results allow to use existing limiters in order to restore positivity, and discrete maximum principles of degrees-of-freedom within elements. Numerical experiments in one and two space dimensions on flows with discontinuous solutions support the conclusions of our analysis and highlight stability, robustness and accuracy of the DGSEM scheme with either CP, or EC fluctuations, while the scheme with CP fluctuations is shown to offer better resolution capabilities. (10.1016/j.jcp.2022.111693)
  DOI : 10.1016/j.jcp.2022.111693
• TVD analysis of a (pseudo-)staggered scheme for the isentropic Euler equations
  
  • Ait-Ameur Katia
  • Ndjinga Michael
  Springer Proceedings in Mathematics & Statistics, Springer, 2023, 433, pp.249-257. In this paper, we build and analyze the stability of a collocated scheme, involving a specific numerical diffusion operator, for the isentropic Euler equations. This scheme is based on the numerical diffusion operator of a family of staggered finite volume schemes introduced in [1]. The properties of this operator allowed to understand the L 2-stability of staggered finite volume methods. Staggered schemes are popular in the thermal hydraulics community for their reported robustness and lack of spurious oscillations. The contributions of this paper are twofold: we firstly build a colocated scheme with a staggered-based numerical diffusion operator, hence the proposed terminology of "pseudo-staggered" scheme, and we secondly present a rigorous TVD analysis, in order to explain the non-oscillatory behaviour of staggered discretisations. (10.1007/978-3-031-40860-1_26)
  DOI : 10.1007/978-3-031-40860-1_26
• Continuous and discrete data assimilation with noisy observations for the Rayleigh-Bénard convection: a computational study
  
  • Hammoud Mohamad Abed El Rahman
  • Le Maître Olivier
  • Titi Edriss S
  • Hoteit Ibrahim
  • Knio Omar
  Computational Geosciences, Springer Verlag, 2023, 27, pp.63-79. Obtaining accurate high-resolution representations of model outputs is essential to describe the system dynamics. In general, however, only spatially-and temporally-coarse observations of the system states are available. These observations can also be corrupted by noise. Downscaling is a process/scheme in which one uses coarse scale observations to reconstruct the highresolution solution of the system states. Continuous Data Assimilation (CDA) is a recently introduced downscaling algorithm that constructs an increasingly accurate representation of the system states by continuously nudging the large scales using the coarse observations. We introduce a Discrete Data Assimilation (DDA) algorithm as a downscaling algorithm based on CDA with discrete-in-time nudging. We then investigate the performance of the CDA and DDA algorithms for downscaling noisy observations of the Rayleigh-Bénard convection system in the chaotic regime. In this computational study, a set of noisy observations was generated by perturbing a reference solution with Gaussian noise before downscaling them. The downscaled fields are then assessed using various error-and ensemble-based skill scores. The CDA solution was shown to converge towards the reference solution faster than that of DDA but at the cost of a higher asymptotic error. The numerical results also suggest a quadratic relationship between the 2 error and the noise level for both CDA and DDA. Cubic and quadratic dependences of the DDA and CDA expected errors on the spatial resolution of the observations were obtained, respectively. (10.1007/s10596-022-10180-4)
  DOI : 10.1007/s10596-022-10180-4
• Time reversal of diffusion processes under a finite entropy condition
  
  • Cattiaux Patrick
  • Conforti Giovanni
  • Gentil Ivan
  • Léonard Christian
  Annales de l'Institut Henri Poincaré (B) Probabilités et Statistiques, Institut Henri Poincaré (IHP), 2023, 59 (4), pp.1844-1881. Motivated by entropic optimal transport, time reversal of diffusion processes is revisited. An integration by parts formula is derived for the carré du champ of a Markov process in an abstract space. It leads to a time reversal formula for a wide class of diffusion processes in Rn possibly with singular drifts, extending the already known results in this domain. The proof of the integration by parts formula relies on stochastic derivatives. Then, this formula is applied to compute the semimartingale characteristics of the time-reversed P* of a diffusion measure P provided that the relative entropy of P with respect to another diffusion measure R is finite, and the semimartingale characteristics of the time-reversed R* are known (for instance when the reference path measure R is reversible). As an illustration of the robustness of this method, the integration by parts formula is also employed to derive a time-reversal formula for a random walk on a graph. (10.1214/22-AIHP1320)
  DOI : 10.1214/22-AIHP1320
• The linear sampling method for random sources
  
  • Garnier Josselin
  • Haddar Houssem
  • Montanelli Hadrien
  SIAM Journal on Imaging Sciences, Society for Industrial and Applied Mathematics, 2023, 16 (3), pp.1572-1593. (10.1137/22M1531336)
  DOI : 10.1137/22M1531336
• Inversion of Eddy-Current Signals Using a Level-Set Method and Block Krylov Solvers
  
  • Audibert Lorenzo
  • Girardon Hugo
  • Haddar Houssem
  • Jolivet Pierre
  SIAM Journal on Scientific Computing, Society for Industrial and Applied Mathematics, 2023, 45 (3), pp.B366-B389. The application motivating this work is related to the identification of deposits inside nuclear power plant steam generators using eddy-current probes. We consider a realistic experimental process that relies on the scan of a domain by sweeping along a tube axis a probe made out of coils, playing the role of the sources/receivers. Solving the inverse shape problem associated with these measurements using a least squares method requires solutions to the eddy-current and the adjoint problems for a large number of right-hand sides at each gradient-descent iteration. Additional cost in the forward solver comes from the use of a potential formulation of the problem that has the advantage of being independent from the topology of the conductive media (that may vary during iterations). We use a level-set approach to avoid remeshing and handle unknown topologies. The crucial ingredient in our algorithm is an optimized way of handling high numbers of right-hand sides for iterative solvers of large-scale problems. We first benchmark various block Krylov methods, block GMRES and block BGCRODR, to test their effectiveness compared to their standard counterpart, i.e., GMRES and GCRODR. Then, we propose for BGCRODR a new implementation for recycling information from previously generated Krylov bases that scales better than traditional approaches. This part is independent from the practical inverse problem at hand. The efficiency of the overall inversion procedure is finally demonstrated on realistic synthetic 3D examples. (10.1137/20M1382064)
  DOI : 10.1137/20M1382064
• Variance-based sensitivity analysis of oil spill predictions in the Red Sea region
  
  • Hammoud Mohamad Abed El Rahman
  • Mittal H V R
  • Le Maitre Olivier
  • Hoteit Ibrahim
  • Knio Omar
  Frontiers in Marine Science, Frontiers Media, 2023, 10, pp.1185106. To support accidental spill rapid response efforts, oil spill simulations may generally need to account for uncertainties concerning the nature and properties of the spill, which compound those inherent in model parameterizations. A full detailed account of these sources of uncertainty would however require prohibitive resources needed to sample a large dimensional space. In this work, a variance based sensitivity analysis is conducted to explore the possibility of restricting a priori the set of uncertain parameters, at least in the context of realistic simulations of oil spills in the Red Sea region spanning a two-week period following the oil release. The evolution of the spill is described using the simulation capabilities of Modelo Hidrodinamico, driven by high-resolution metocean fields of the Red Sea (RS) was adopted to simulate accidental oil spills in the RS. Eight spill scenarios are considered in the analysis, which are carefully selected to account for the diversity of meto-cean conditions in the region. Polynomial chaos expansions are employed to propagate parametric uncertainties and efficiently estimate variance-based sensitivities. Attention is focused on integral quantities characterizing the transport, deformation, evaporation and dispersion of the spill. The analysis indicates that variability in these quantities may be suitably captured by restricting the set of uncertain inputs parameters, namely the wind coefficient, interfacial tension, API gravity, and viscosity. Thus, forecast variability and confidence intervals may be reasonably estimated in the corresponding four dimensional input space. (10.3389/fmars.2023.1185106)
  DOI : 10.3389/fmars.2023.1185106
• Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms
  
  • Plassier Vincent
  • Durmus Alain
  • Moulines Eric
  , 2023. This paper focuses on Bayesian inference in a federated learning context (FL). While several distributed MCMC algorithms have been proposed, few consider the specific limitations of FL such as communication bottlenecks and statistical heterogeneity. Recently, Federated Averaging Langevin Dynamics (FALD) was introduced, which extends the Federated Averaging algorithm to Bayesian inference. We obtain a novel tight non-asymptotic upper bound on the Wasserstein distance to the global posterior for FALD. This bound highlights the effects of statistical heterogeneity, which causes a drift in the local updates that negatively impacts convergence. We propose a new algorithm VR-FALD* that uses control variates to correct the client drift. We establish non-asymptotic bounds showing that VR-FALD* is not affected by statistical heterogeneity. Finally, we illustrate our results on several FL benchmarks for Bayesian inference.
• Stochastic Variable Metric Proximal Gradient with variance reduction for non-convex composite optimization
  
  • Fort Gersende
  • Moulines Eric
  Statistics and Computing, Springer Verlag (Germany), 2023, 33, pp.65. This paper introduces a novel algorithm, the Perturbed Proximal Preconditioned SPIDER algorithm (3P-SPIDER), designed to solve finite sum non-convex composite optimization. It is a stochastic Variable Metric Forward-Backward algorithm, which allows approximate preconditioned forward operator and uses a variable metric proximity operator as the backward operator; it also proposes a mini-batch strategy with variance reduction to address the finite sum setting. We show that 3P-SPIDER extends some Stochastic preconditioned Gradient Descent-based algorithms and some Incremental Expectation Maximization algorithms to composite optimization and to the case the forward operator can not be computed in closed form. We also provide an explicit control of convergence in expectation of 3P-SPIDER, and study its complexity in order to satisfy the epsilon-approximate stationary condition. Our results are the first to combine the composite non-convex optimization setting, a variance reduction technique to tackle the finite sum setting by using a minibatch strategy and, to allow deterministic or random approximations of the preconditioned forward operator. Finally, through an application to inference in a logistic regression model with random effects, we numerically compare 3P-SPIDER to other stochastic forward-backward algorithms and discuss the role of some design parameters of 3P-SPIDER. (10.1007/s11222-023-10230-6)
  DOI : 10.1007/s11222-023-10230-6
• Human-environment feedback and the consistency of proenvironmental behavior
  
  • Ecotière Claire
  • Billiard Sylvain
  • André Jean-Baptiste
  • Collet Pierre
  • Ferrière Régis
  • Méléard Sylvie
  PLoS Computational Biology, PLOS, 2023, 19 (9), pp.e1011429. Addressing global environmental crises such as anthropogenic climate change requires the consistent adoption of proenvironmental behavior by a large part of a population. Here, we develop a mathematical model of a simple behavior-environment feedback loop to ask how the individual assessment of the environmental state combines with social interactions to influence the consistent adoption of proenvironmental behavior, and how this feeds back to the perceived environmental state. In this stochastic individual-based model, individuals can switch between two behaviors, ‘active’ (or actively proenvironmental) and ‘baseline’, differing in their perceived cost (higher for the active behavior) and environmental impact (lower for the active behavior). We show that the deterministic dynamics and the stochastic fluctuations of the system can be approximated by ordinary differential equations and a Ornstein-Uhlenbeck type process. By definition, the proenvironmental behavior is adopted consistently when, at population stationary state, its frequency is high and random fluctuations in frequency are small. We find that the combination of social and environmental feedbacks can promote the spread of costly proenvironmental behavior when neither, operating in isolation, would. To be adopted consistently, strong social pressure for proenvironmental action is necessary but not sufficient—social interactions must occur on a faster timescale compared to individual assessment, and the difference in environmental impact must be small. This simple model suggests a scenario to achieve large reductions in environmental impact, which involves incrementally more active and potentially more costly behavior being consistently adopted under increasing social pressure for proenvironmentalism. (10.1371/journal.pcbi.1011429)
  DOI : 10.1371/journal.pcbi.1011429