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A line search method with variable sample size
Vrijeme: 
13.6.2012 17:00 
Predavaonica: 
005 
Predavač: 
Nataša Krejić, Department of Mathematics and Informatics, University of Novi Sad, Serbia 
Naziv: 
A line search method with variable sample size 
Opis:
Minimization of an unconstrained objective function in the form of math
ematical expectation is considered. Sample Average Approximation  SAA
method transforms the expectation objective function into a realvalued de
terministic function using large sample in each iteration and thus deal with
deterministic function minimization. The main drawback of this approach is
its cost. A large sample of the random variable that defines the expectation
must be taken in order to get reasonably good approximation and thus the
sample average approximation method assumes very large number of func
tional evaluations. We will present a line search strategy that uses variable
sample size and thus makes the process significantly cheaper. Two measures
of progress  lack of precision and functional decrease are calculated at each
iteration. Based on this two measures a new sample size is determined. The
rule we will present allows us to increase or decrease the sample size in each
iteration until we reach some neighborhood of the solution. After that the
maximal sample size is used so the variable sample size strategy generates the
solution of the same quality as SAA method but with significantly smaller
number of functional evaluations.

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