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Displaying 881 –
891 of
891
We introduce augmented Lagrangian methods for solving finite dimensional variational inequality problems
whose feasible sets are defined by convex inequalities, generalizing the proximal augmented Lagrangian method
for constrained optimization. At each iteration, primal variables are updated by solving
an unconstrained variational inequality problem, and then dual variables are updated through a closed formula.
A full convergence analysis is provided, allowing for inexact solution of the subproblems.
...
Data collected by statistical offices generally contain errors, which have to be corrected before reliable data can be published. This correction process is referred to as statistical data editing. At statistical offices, certain rules, so-called edits, are often used during the editing process to determine whether a record is consistent or not. Inconsistent records are considered to contain errors, while consistent records are considered error-free. In this article we focus on automatic error localisation...
Cet article est la suite de l’article «Autour de nouvelles notions pour l’analyse des algorithmes d’approximation : formalisme unifié et classes d’approximation» où nous avons présenté et discuté, dans le cadre d’un nouveau formalisme pour l’approximation polynomiale (algorithmique polynomiale à garanties de performances pour des problèmes NP-difficiles), des outils permettant d’évaluer, dans l’absolu, les proporiétés d’approximation de problèmes difficiles. Afin de répondre pleinement à l’objectif...
This paper is the continuation of the paper “Autour
de nouvelles notions pour l'analyse des
algorithmes d'approximation: Formalisme unifié et classes
d'approximation” where a new formalism for polynomial
approximation and its basic tools allowing an “absolute”
(individual) evaluation the approximability properties of
NP-hard problems have been presented and discussed. In
order to be used for exhibiting a structure for the
class NPO (the optimization problems of NP),
these tools must be enriched...
This paper considers discrete-time Markov control processes on Borel spaces, with possibly unbounded costs, and the long run average cost (AC) criterion. Under appropriate hypotheses on weighted norms for the cost function and the transition law, the existence of solutions to the average cost optimality inequality and the average cost optimality equation are shown, which in turn yield the existence of AC-optimal and AC-canonical policies respectively.
This paper shows the convergence of the value iteration (or successive approximations) algorithm for average cost (AC) Markov control processes on Borel spaces, with possibly unbounded cost, under appropriate hypotheses on weighted norms for the cost function and the transition law. It is also shown that the aforementioned convergence implies strong forms of AC-optimality and the existence of forecast horizons.
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