A proximal approach to the inversion of ill-conditioned matrices

We propose a general proximal algorithm for the inversion of ill-conditioned matrices. This algorithm is based on a variational characterization of pseudo-inverses. We show that a particular instance of it (with constant regularization parameter) belongs to the class of {\sl fixed point} methods. Convergence of the algorithm is also discussed

An hybrid system approach to nonlinear optimal control problems

We consider a nonlinear ordinary differential equation and want to control its behavior so that it reaches a target by minimizing a cost function. Our approach is to use hybrid systems to solve this problem: the complex dynamic is replaced by piecewise affine approximations which allow an analytical resolution. The sequence of affine models then forms a sequence of states of a hybrid automaton. Given a sequence of states, we introduce an hybrid approximation of the nonlinear controllable domain and propose a new algorithm computing a controllable, piecewise convex approximation. The same way the nonlinear optimal control problem is replaced by an hybrid piecewise affine one. Stating a hybrid maximum principle suitable to our hybrid model, we deduce the global structure of the hybrid optimal control steering the system to the target

Computing the Kalman form

We present two algorithms for the computation of the Kalman form of a linear control system. The first one is based on the technique developed by Keller-Gehrig for the computation of the characteristic polynomial. The cost is a logarithmic number of matrix multiplications. To our knowledge, this improves the best previously known algebraic complexity by an order of magnitude. Then we also present a cubic algorithm proven to more efficient in practice.Comment: 10 page

Robust Measurement Feedback Control of an Inclined Cable

International audienceConsidering the partial differential equation model of the vibrations of an inclined cable, we are interested in applying robust control technics to stabilize the system with measurement feedback when it is submitted to external disturbances. This paper focuses indeed on the construction of a standard linear infinite dimensional state space system and an H_infinity feedback control of vibrations with partial observation of the state. The control and observation are performed using an active tendon

A New Mixed Iterative Algorithm to Solve the Fuel-Optimal Linear Impulsive Rendezvous Problem

International audienceThe optimal fuel impulsive time-fixed rendezvous problem is reviewed. In a linear setting, it may be reformulated as a non convex polynomial optimization problem for a pre-specified fixed number of velocity increments. Relying on variational results previously published in the literature, an improved mixed iterative algorithm is defined to address the issue of optimization over the number of impulses. Revisiting the primer vector theory, it combines variational tests with sophisticated numerical tools from algebraic geometry to solve polynomial necessary and sufficient conditions of optimality. Numerical examples under circular and elliptic assumptions show that this algorithm is efficient and can be integrated into a rendezvous planning tool

Parameter-Free FISTA by Adaptive Restart and Backtracking

We consider a combined restarting and adaptive backtracking strategy for the popular Fast Iterative Shrinking-Thresholding Algorithm frequently employed for accelerating the convergence speed of large-scale structured convex optimization problems. Several variants of FISTA enjoy a provable linear convergence rate for the function values $F(x_n)$ of the form $\mathcal{O}( e^{-K\sqrt{\mu/L}~n})$ under the prior knowledge of problem conditioning, i.e. of the ratio between the (\L ojasiewicz) parameter $\mu$ determining the growth of the objective function and the Lipschitz constant $L$ of its smooth component. These parameters are nonetheless hard to estimate in many practical cases. Recent works address the problem by estimating either parameter via suitable adaptive strategies. In our work both parameters can be estimated at the same time by means of an algorithmic restarting scheme where, at each restart, a non-monotone estimation of $L$ is performed. For this scheme, theoretical convergence results are proved, showing that a $\mathcal{O}( e^{-K\sqrt{\mu/L}n})$ convergence speed can still be achieved along with quantitative estimates of the conditioning. The resulting Free-FISTA algorithm is therefore parameter-free. Several numerical results are reported to confirm the practical interest of its use in many exemplar problems

A Power Series Expansion based Method to compute the Probability of Collision for Short-term Space Encounters

Rapport LAAS n° 15072This article provides a new method for computing the probability of collision between two spherical space objects involved in a short-term encounter under Gaussian-distributed uncertainty. In this model of conjunction, classical assumptions reduce the probability of collision to the integral of a two-dimensional Gaussian probability density function over a disk. The computational method presented here is based on an analytic expression for the integral, derived by use of Laplace transform and D-finite functions properties. The formula has the form of a product between an exponential term and a convergent power series with positive coefficients. Analytic bounds on the truncation error are also derived and are used to obtain a very accurate algorithm. Another contribution is the derivation of analytic bounds on the probability of collision itself, allowing for a very fast and - in most cases - very precise evaluation of the risk. The only other analytical method of the literature - based on an approximation - is shown to be a special case of the new formula. A numerical study illustrates the efficiency of the proposed algorithms on a broad variety of examples and favorably compares the approach to the other methods of the literature

Algorithmes hybrides pour le contrôle optimal des systèmes non linéaires

This thesis is devoted to the analysis of non linear control problems by hybrid computation methods. We defend the idea that the use of hybrid systems allows the approximate resolution of non linear control problems without any a priori knowledge of the behavior of the considered systems. In the first part we focus on the modelling of non linear control systems by a new class of piecewise affine hybrid systems. Particularly, we propose a full analysis of the error and of the convergence of the hybrid approximation. The second part deals with the controllability to the origin of non linear systems. We first provide an analysis of the approximation error between the non linear controllable domain and its hybrid approximation. We then propose a constructive approach for the computation of the controllable domain which allows to reduce the exploration of the discrete states of the hybrid automaton. The last part is devoted to the search of optimal solutions of the non linear and hybrid optimal control problems. We first justify the relevance of our hybrid model thanks to two approaches: the Pontryagin maximum principle and viscosity solutions of Hamilton-Jacobi-Bellman equations. Particularly, we state a hybrid maximum principle which provides us the structure of the hybrid optimal control. These three parts answer to the main purpose : to develop by the hybrid computation combining numerical analysis and computer algebra, some mathematical and algorithmic efficient tools for the analysis of non linear control systems.Cette thèse est consacrée à la résolution des problèmes de contrôle non linéaires par des méthodes de calcul hybride. L'idée défendue est que la modélisation par les systèmes hybrides permet la résolution approchée des problèmes non linéaires sans connaissance a priori du comportement du système étudié. Dans un premier temps, nous nous intéressons à la modélisation des systèmes de contrôle non linéaires par une nouvelle classe de systèmes hybrides affines par morceaux. Un soin particulier est apporté à l'étude de l'erreur et de la convergence de l'approximation hybride. La deuxième partie est consacrée au problème de la contrôlabilité à l'origine des systèmes non linéaires. Nous nous intéressons tout d'abord à la quantification de l'erreur commise entre le domaine contrôlable non linéaire et son approximation hybride. Nous proposons ensuite une approche constructive pour le calcul du domaine contrôlable, permettant alors de réduire l'exploration des états discrets de l'automate hybride. La dernière partie est dédiée à la recherche de solutions optimales des problèmes de contrôle non linéaires et hybrides. Nous justifions tout d'abord la pertinence de la modélisation hybride à travers deux approches : le principe du maximum de Pontryagin et les solutions de viscosité des équations d'Hamilton-Jacobi-Bellman. Nous énonçons en particulier un principe du maximum hybride qui nous permet alors de déterminer la structure du contrôle optimal hybride. Ces trois parties répondent à un objectif principal : développer par le calcul hybride combinant analyse numérique et calcul formel, des outils mathématiques et algorithmiques efficaces pour l'étude de dynamiques contrôlées non linéaires