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Uncontrolled inexact information within bundle methods

Jérôme Malick (), Welington Oliveira () and Sofia Zaourar ()
Additional contact information
Jérôme Malick: CNRS, LJK
Welington Oliveira: UERJ
Sofia Zaourar: INRIA, UJF

EURO Journal on Computational Optimization, 2017, vol. 5, issue 1, No 2, 5-29

Abstract: Abstract We consider convex non-smooth optimization problems where additional information with uncontrolled accuracy is readily available. It is often the case when the objective function is itself the output of an optimization solver, as for large-scale energy optimization problems tackled by decomposition. In this paper, we study how to incorporate the uncontrolled linearizations into (proximal and level) bundle algorithms in view of generating better iterates and possibly accelerating the methods. We provide the convergence analysis of the algorithms using uncontrolled linearizations, and we present numerical illustrations showing they indeed speed up resolution of two stochastic optimization problems coming from energy optimization (two-stage linear problems and chance-constrained problems in reservoir management).

Keywords: Non-smooth optimization; Bundle methods; Inexact oracle; Energy optimization; Two-stage stochastic problems; Chance-constrained problems; 65K05; 49J52; 49M27; 90C15; 90C25; 90C27 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s13675-015-0060-9

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