Iterative methods for solving the dual formulation arising from image restoration.
Iterative Optimierungsverfahren, die unter schwachen Voraussetzungen konvergieren.
Iterative solution of unstable variational inequalities on approximately given sets.
Kernel-function Based Primal-Dual Algorithms for P*(κ) Linear Complementarity Problems
Recently, [Y.Q. Bai, M. El Ghami and C. Roos, SIAM J. Opt. 15 (2004) 101–128] investigated a new class of kernel functions which differs from the class of self-regular kernel functions. The class is defined by some simple conditions on the growth and the barrier behavior of the kernel function. In this paper we generalize the analysis presented in the above paper for P*(κ) Linear Complementarity Problems (LCPs). The analysis for LCPs deviates significantly from the analysis for linear optimization....
-error estimate for a discrete two-sided obstacle problem and multilevel projective algorithm.
La différentiation automatique et son utilisation en optimisation
In this work, we present an introduction to automatic differentiation, its use in optimization software, and some new potential usages. We focus on the potential of this technique in optimization. We do not dive deeply in the intricacies of automatic differentiation, but put forward its key ideas. We sketch a survey, as of today, of automatic differentiation software, but warn the reader that the situation with respect to software evolves rapidly. In the last part of the paper, we present some...
La programmation mathématique multicritère et la gestion des ressources en eau
L'affectation exponentielle et le problème du plus court chemin dans un graphe
Lagrangean decomposition for integer programming : theory and applications
Large quadratic programming problems generated by rigid body simulation.
Large-scale Kalman filtering using the limited memory BFGS method.
Large-scale nonlinear programming algorithm using projection methods
A method for solving large convex optimization problems is presented. Such problems usually contain a big linear part and only a small or medium nonlinear part. The parts are tackled using two specialized (and thus efficient) external solvers: purely nonlinear and large-scale linear with a quadratic goal function. The decomposition uses an alteration of projection methods. The construction of the method is based on the zigzagging phenomenon and yields a non-asymptotic convergence, not dependent...
Le traitement des solutions quasi optimales en programmation linéaire continue : une synthèse
Least regret control, virtual control and decomposition methods
Least regret control, virtual control and decomposition methods
"Least regret control" consists in trying to find a control which "optimizes the situation" with the constraint of not making things too worse with respect to a known reference control, in presence of more or less significant perturbations. This notion was introduced in [7]. It is recalled on a simple example (an elliptic system, with distributed control and boundary perturbation) in Section 2. We show that the problem reduces to a standard optimal control problem for augmented state equations. On...
Least Squares with a Quadratic Constraint.
L...-Error Estimate for an Approximation of a Parabolic Variational Inequality.
Limited-memory variable metric methods that use quantities from the preceding iteration
Line Search by Curve Fitting in Minimization Algorithms.
Linear complementarity problem and extremal hyperplanes