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Ci-dessous, les différences entre deux révisions de la page.

--- groupe-dfo-bbo:acteurs:students:doc:kwassi-joseph-dzahini:main-page [2020/05/07 01:39]
dzahkwas
+++ groupe-dfo-bbo:acteurs:students:doc:kwassi-joseph-dzahini:main-page [2020/08/21 12:22] (Version actuelle)
dzahkwas [Meetings]
@@ Ligne 15: / Ligne 15: @@
 Click [[https://www.gerad.ca/fr/papers/G-2019-30/view|here]] to access the manuscript.
-==== Expected complexity analysis of stochastic direct-search ====
+===== Expected complexity analysis of stochastic direct-search =====
+This work presents the convergence rate analysis of stochastic variants of the broad class of direct-search methods of directional type. It introduces an algorithm designed to optimize differentiable objective functions f whose values can only be computed through a stochastically noisy blackbox. The proposed stochastic directional direct-search (SDDS) algorithm accepts new iterates by imposing a sufficient decrease condition on so called probabilistic estimates of the corresponding unavailable objective function values. The accuracy of such estimates is required to hold with a sufficiently large but fixed probability β. The analysis of this method utilizes an existing supermartingale-based framework proposed for the convergence rates analysis of stochastic optimization methods that use adaptive step sizes. It aims to show that the expected number of iterations required to drive the norm of the gradient of f below a given threshold ϵ is bounded in **O**(ϵ^(−p/min(p−1,1))/(2β−1)) with p>1. Unlike prior analysis using the same aforementioned framework such as those of stochastic trust-region methods and stochastic line search methods, SDDS does not use any gradient information to find descent directions. However, its convergence rate is similar to those of both latter methods with a dependence on ϵ that also matches that of the broad class of deterministic directional direct-search methods which accept new iterates by imposing a sufficient decrease condition.
+Click [[https://www.gerad.ca/fr/papers/G-2020-18/view|here]] to access the manuscript.
+===== Constrained stochastic blackbox optimization with MADS and the progressive barrier =====
+Current project: the paper will be available soon.
+===== Meetings =====
+  * [[.:dossier:sous-page|Meeting of August 21st 2020]]
+===== Monitoring of works =====
+  * [[.:feedback-article1:drafting-manuscript-1| First manuscript (StoMADS) ]]
+  * [[.:feedback-article2:drafting-manuscript-2| Second manuscript (Expected complexity) ]]
+  * [[.:feedback-article3:drafting-manuscript-3| Third manuscript (StoMADS-PB) ]]
+  * [[.:feedback-thesis:drafting-thesis| Thesis (Stochastic blackbox optimization) ]]
-This work presents the convergence rate analysis of stochastic variants of the broad class of direct-search methods of directional type. It introduces an algorithm designed to optimize differentiable objective functions f whose values can only be computed through a stochastically noisy blackbox. The proposed stochastic directional direct-search (SDDS) algorithm accepts new iterates by imposing a sufficient decrease condition on so called probabilistic estimates of the corresponding unavailable objective function values. The accuracy of such estimates is required to hold with a sufficiently large but fixed probability β. The analysis of this method utilizes an existing supermartingale-based framework proposed for the convergence rates analysis of stochastic optimization methods that use adaptive step sizes. It aims to show that the expected number of iterations required to drive the norm of the gradient of f below a given threshold ϵ is bounded in
-[[.:dossier:sous-page|nom]]
-[[https://polymtl.webex.com/polymtl/j.php?MTID=m1127150fc7c1943b782ea7c167416202|lien externe]]