Section: Research Program
Main research topics
Stochastic modeling: Markov chain, Piecewise Deterministic Markov Processes (PDMP), Markov Decision Processes (MDP).
The mathematical representation of complex systems is a preliminary step to our final goal corresponding to the optimization of its performance. The team CQFD focuses on two complementary types of approaches. The first approach is based on mathematical representations built upon physical models where the dynamic of the real system is described by stochastic processes. The second one consists in studying the modeling issue in an abstract framework where the real system is considered as blackbox. In this context, the outputs of the system are related to its inputs through a statistical model. Regarding stochastic processes, the team studies Piecewise Deterministic Markov Processes (PDMPs) and Markov Decision Processes (MDPs). These two classes of Markov processes form general families of controlled stochastic models suitable for the design of sequential decisionmaking problems. They appear in many fields such as biology, engineering, computer science, economics, operations research and provide powerful classes of processes for the modeling of complex systems. Our contribution to this topic consists in expressing reallife industrial problems into these mathematical frameworks. Regarding statistical methods, the team works on dimension reduction models. They provide a way to understand and visualize the structure of complex data sets. Furthermore, they are important tools in several different areas such as data analysis and machine learning, and appear in many applications such as biology, genetics, environment and recommendation systems. Our contribution to this topic consists in studying semiparametric modeling which combines the advantages of parametric and nonparametric models.
Estimation methods: estimation for PDMP; estimation in non and semi parametric regression modeling.
To the best of our knowledge, there does not exist any general theory for the problems of estimating parameters of PDMPs although there already exist a large number of tools for subclasses of PDMPs such as point processes and marked point processes. To fill the gap between these specific models and the general class of PDMPs, new theoretical and mathematical developments will be on the agenda of the whole team. In the framework of nonparametric regression or quantile regression, we focus on kernel estimators or kernel local linear estimators for complete data or censored data. New strategies for estimating semiparametric models via recursive estimation procedures have also received an increasing interest recently. The advantage of the recursive estimation approach is to take into account the successive arrivals of the information and to refine, step after step, the implemented estimation algorithms. These recursive methods do require restarting calculation of parameter estimation from scratch when new data are added to the base. The idea is to use only the previous estimations and the new data to refresh the estimation. The gain in time could be very interesting and there are many applications of such approaches.
Dimension reduction: dimensionreduction via SIR and related methods, dimensionreduction via multidimensional and classification methods.
Most of the dimension reduction approaches seek for lower dimensional subspaces minimizing the loss of some statistical information. This can be achieved in modeling framework or in exploratory data analysis context.
In modeling framework we focus our attention on semiparametric models in order to conjugate the advantages of parametric and nonparametric modeling. On the one hand, the parametric part of the model allows a suitable interpretation for the user. On the other hand, the functional part of the model offers a lot of flexibility. In this project, we are especially interested in the semiparametric regression model $Y=f\left({X}^{\text{'}}\theta \right)+\epsilon ,$ the unknown parameter $\theta $ belongs to ${\mathbb{R}}^{p}$ for a single index model, or is such that $\theta =[{\theta}_{1},\cdots ,{\theta}_{d}]$ (where each ${\theta}_{k}$ belongs to ${\mathbb{R}}^{p}$ and $d\le p$ for a multiple indices model), the noise $\epsilon $ is a random error with unknown distribution, and the link function $f$ is an unknown real valued function. Another way to see this model is the following: the variables $X$ and $Y$ are independent given ${X}^{\text{'}}\theta \phantom{\rule{0.166667em}{0ex}}$. In our semiparametric framework, the main objectives are to estimate the parametric part $\theta $ as well as the nonparametric part which can be the link function $f$, the conditional distribution function of $Y$ given $X$ or the conditional quantile ${q}_{\alpha}$. In order to estimate the dimension reduction parameter $\theta $ we focus on the Sliced Inverse Regression (SIR) method which has been introduced by Li [53] and Duan and Li [51].
Methods of dimension reduction are also important tools in the field of data analysis, data mining and machine learning.They provide a way to understand and visualize the structure of complex data sets.Traditional methods among others are principal component analysis for quantitative variables or multiple component analysis for qualitative variables. New techniques have also been proposed to address these challenging tasks involving many irrelevant and redundant variables and often comparably few observation units. In this context, we focus on the problem of synthetic variables construction, whose goals include increasing the predictor performance and building more compact variables subsets. Clustering of variables is used for feature construction. The idea is to replace a group of ”similar” variables by a cluster centroid, which becomes a feature. The most popular algorithms include Kmeans and hierarchical clustering. For a review, see, e.g., the textbook of Duda [52].
Stochastic control: optimal stopping, impulse control, continuous control, linear programming.
The main objective is to develop approximation techniques to provide quasioptimal feasible solutions and to derive optimality results for control problems related to MDPs and PDMPs:

Approximation techniques. The analysis and the resolution of such decision models mainly rely on the maximum principle and/or the dynamic/linear programming techniques together with their various extensions such as the value iteration (VIA) and the policy iteration (PIA) algorithm. However, it is well known that these approaches are hardly applicable in practice and suffer from the socalled curse of dimensionality. Hence, solving numerically a PDMP or an MDP is a difficult and important challenge. Our goal is to obtain results which are both consistent from a theoretical point of view and computationally tractable and accurate from an application standpoint. It is important to emphasize that these research objectives were not planned in our initial 2009 program.
Our objective is to propose approximation techniques to efficiently compute the optimal value function and to get quasioptimal controls for different classes of constrained and unconstrained MDPs with general state/action spaces, and possibly unbounded cost function. Our approach is based on combining the linear programming formulation of an MDP with probabilistic approximation techniques related to quantization techniques and the theory of empirical processes. An other aim is to apply our methods to specific industrial applications in collaboration with industrial partners such as Airbus Defence & Space, DCNS and Thales.
Asymptotic approximations are also developed in the context of queueing networks, a class of models where the decision policy of the underlying MDP is in some sense fixed a priori, and our main goal is to study the transient or stationary behavior of the induced Markov process. Even though the decision policy is fixed, these models usually remain intractable to solve. Given this complexity, the team has developed analyses in some limiting regime of practical interest, i.e., queueing models in the largenetwork, heavytraffic, fluid or meanfield limit. This approach is helpful to obtain a simpler mathematical description of the system under investigation, which is often given in terms of ordinary differential equations or convex optimization problems.

Optimality results. Our aim is to investigate new important classes of optimal stochastic control problems including constraints and combining continuous and impulse actions for MDPs and PDMPs. In this framework, our objective is to obtain different types of optimality results. For example, we intend to provide conditions to guarantee the existence and uniqueness of the optimality equation for the problem under consideration and to ensure existence of an optimal (and $\u03f5$optimal) control strategy. We also plan to analyze the structural properties of the optimal strategies as well as to study the associated infinite dimensional linear programming problem. These results can be seen as a first step toward the development of numerical approximation techniques in the sense described above.