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hal-00322795, version 1

Optimal experimental design and quadratic optimization

Rebecca Haycroft () 1, Luc Pronzato () 2, Henry P. Wynn () 3, Anatoly A. Zhigljavsky () 1

ProbaStat 2006 39 (2006) 115-123

Abstract: A well known gradient-type algorithm for solving quadratic optimization problems is the method of Steepest Descent. Here the Steepest Descent algorithm is generalized to a broader family of gradient algorithms, where the step-length is chosen in accordance with a particular procedure. The asymptotic rate of convergence of this family is studied. To facilitate the investigation, we re-write the algorithms in a normalized form which enables us to exploit a link with theory of optimum experimental design.

  • 1:  Cardiff University, School of Mathematics
  • Cardiff University
  • 2:  Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe SYSTEMES
  • Université Nice Sophia Antipolis [UNS] – CNRS : UMR7271
  • 3:  London School of Economics, Department of Statistics
  • London School of Economics
  • Domain : Mathematics/Statistics
    Statistics/Statistics Theory
  • Keywords : gradient algorithms – steepest descent algorithm – rate of convergence – design of experiments – optimality criteria
 
  • hal-00322795, version 1
  • http://hal.archives-ouvertes.fr/hal-00322795
  • oai:hal.archives-ouvertes.fr:hal-00322795
  • From: Luc Pronzato <>
  • Submitted on: Thursday, 18 September 2008 16:17:55
  • Updated on: Friday, 21 November 2008 10:03:18