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Because the convergence analysis is performed pathwisely, we are able to obtain our results in a flexible setting, which requires mild assumptions on the distributions and which includes the possibility of using different sampling distributions along the algorithm. We illustrate these ideas by studying a modification of the well-known\n pure random search<\/jats:italic>\n method, adapting it to the variable-sample scheme, and show conditions for convergence of the algorithm. Implementation issues are discussed and numerical results are presented to illustrate the ideas.\n <\/jats:p>","DOI":"10.1145\/858481.858483","type":"journal-article","created":{"date-parts":[[2003,8,5]],"date-time":"2003-08-05T15:03:25Z","timestamp":1060095805000},"page":"108-133","source":"Crossref","is-referenced-by-count":84,"title":["Variable-sample methods for stochastic optimization"],"prefix":"10.1145","volume":"13","author":[{"given":"Tito","family":"Homem-De-Mello","sequence":"first","affiliation":[{"name":"Ohio State University, Columbus, OH"}]}],"member":"320","reference":[{"key":"e_1_2_1_1_1","unstructured":"Allen T. Ittiwattana W. and Bernshteyn M. 2002. An elitist genetic algorithm incorporating sequential subset selection. Manuscript Ohio State University.]] Allen T. Ittiwattana W. and Bernshteyn M. 2002. An elitist genetic algorithm incorporating sequential subset selection. 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