Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/20781
Title: Inexact subgradient methods for quasi-convex optimization problems
Authors: Hu, Y
Yang, X 
Sim, CK
Keywords: Noise
Quasi-convex optimization
Subgradient method
Weak sharp minima
Issue Date: 2015
Publisher: Elsevier
Source: European journal of operational research, 2015, v. 240, no. 2, p. 315-327 How to cite?
Journal: European journal of operational research 
Abstract: In this paper, we consider a generic inexact subgradient algorithm to solve a nondifferentiable quasiconvex constrained optimization problem. The inexactness stems from computation errors and noise, which come from practical considerations and applications. Assuming that the computational errors and noise are deterministic and bounded, we study the effect of the inexactness on the subgradient method when the constraint set is compact or the objective function has a set of generalized weak sharp minima. In both cases, using the constant and diminishing stepsize rules, we describe convergence results in both objective values and iterates, and finite convergence to approximate optimality. We also investigate efficiency estimates of iterates and apply the inexact subgradient algorithm to solve the Cobb-Douglas production efficiency problem. The numerical results verify our theoretical analysis and show the high efficiency of our proposed algorithm, especially for the large-scale problems.
URI: http://hdl.handle.net/10397/20781
ISSN: 0377-2217
EISSN: 1872-6860
DOI: 10.1016/j.ejor.2014.05.017
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