Optimization of the quantile criterion for the convex loss function by a stochastic quasigradient algorithm

Andrey Kibzun () and Evgeniy Matveev ()

Annals of Operations Research, 2012, vol. 200, issue 1, 183-198

Abstract: A stochastic quasigradient algorithm is suggested for solving the quantile optimization problem with a convex loss function. The algorithm is based on stochastic finite-difference approximations of gradients of the quantile function by using the order statistics. The algorithm convergence almost surely is proved. Copyright Springer Science+Business Media, LLC 2012

Keywords: Stochastic quasigradient algorithm; Quantile function; Order statistics; The Minkowski inequality; Convergence almost surely; Quasiconcavity of the probability measure (search for similar items in EconPapers)
Date: 2012
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DOI: 10.1007/s10479-011-0987-z

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