-minimal pairs of compact convex sets.
Caractérisation de la convergence au sens de Mosco en terme d'approximations inf-convolutives
Chance constrained optimal beam design: Convex reformulation and probabilistic robust design
In this paper, we are concerned with a civil engineering application of optimization, namely the optimal design of a loaded beam. The developed optimization model includes ODE-type constraints and chance constraints. We use the finite element method (FEM) for the approximation of the ODE constraints. We derive a convex reformulation that transforms the problem into a linear one and find its analytic solution. Afterwards, we impose chance constraints on the stress and the deflection of the beam....
Characterizing Optimality in Nonconvex Optimizationi
Combined trust region methods for nonlinear least squares
Comparaison expérimentale d'algorithmes pour les problèmes de recouvrement et de maximisation d'une fonction pseudo-booléenne
Computational experience with improved conjugate gradient methods for unconstrained minimization
Computational experience with improved variable metric methods for unconstrained minimization
Configuring a sensor network for fault detection in distributed parameter systems
The problem of fault detection in distributed parameter systems (DPSs) is formulated as that of maximizing the power of a parametric hypothesis test which checks whether or not system parameters have nominal values. A computational scheme is provided for the design of a network of observation locations in a spatial domain that are supposed to be used while detecting changes in the underlying parameters of a distributed parameter system. The setting considered relates to a situation where from among...
Conjugate direction algorithms for extended conic functions
Conjugate gradient algorithms for conic functions
The paper contains a description and an analysis of two modifications of the conjugate gradient method for unconstrained minimization which find a minimum of the conic function after a finite number of steps. Moreover, further extension of the conjugate gradient method is given which is based on a more general class of the model functions.
Constrained optimization via unconstrained minimization
Constraint consensus methods for finding interior feasible points in second-order cones.
Construction de facettes pour le polytope du sac-à-dos quadratique en 0-1
Construction de facettes pour le polytope du sac-à-dos quadratique en 0-1
Nous construisons des familles de facettes du polytope du sac-à-dos quadratique en 0-1 selon les deux approches suivantes. Le Boolean quadric polytope (introduit dans le cas sans contraintes par Padberg [12]) contenant le polytope du sac-à-dos quadratique, une première approche consiste à se demander sous quelles conditions une facette du premier est aussi une facette du second et quand ces conditions ne sont pas remplies quels liftings permettent d'en faire une facette. Des réponses à ces questions...
Construction de rankings dans un tournoi
Convergence analysis of adaptive trust region methods
In this paper, we propose a new class of adaptive trust region methods for unconstrained optimization problems and develop some convergence properties. In the new algorithms, we use the current iterative information to define a suitable initial trust region radius at each iteration. The initial trust region radius is more reasonable in the sense that the trust region model and the objective function are more consistent at the current iterate. The global convergence, super-linear and quadratic convergence...
Convergence of primal-dual solutions for the nonconvex log-barrier method without LICQ
This paper characterizes completely the behavior of the logarithmic barrier method under a standard second order condition, strict (multivalued) complementarity and MFCQ at a local minimizer. We present direct proofs, based on certain key estimates and few well–known facts on linear and parametric programming, in order to verify existence and Lipschitzian convergence of local primal-dual solutions without applying additionally technical tools arising from Newton–techniques.
Convergence optimale de l'algorithme de «réallocation-recentrage» dans le cas continu le plus simple
Convergence results for a class of abstract continuous descent methods.