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Ergodic results in subgradient optimization

Paper i proceeding
Författare Torbjörn Larsson
Michael Patriksson
Ann-Brith Strömberg
Publicerad i Nonlinear Optimization and Applications
Sidor 229-248
ISBN 9780306453168
Publiceringsår 1996
Publicerad vid Institutionen för matematik
Sidor 229-248
Språk en
Ämnesord Nonsmooth minimization, Conditional subgradient optimization, Ergodic sequences, Lagrange multipliers
Ämneskategorier Optimeringslära, systemteori

Sammanfattning

Subgradient methods are popular tools for nonsmooth, convex minimization, especially in the context of Lagrangean relaxation; their simplicity has been a main contribution to their success. As a consequence of the nonsmoothness, it is not straightforward to monitor the progress of a subgradient method in terms of the approximate fulfilment of optimality conditions, since the subgradients used in the method will, in general, not accumulate to subgradients that verify optimality of a solution obtained in the limit. Further, certain supplementary information, such as convergent estimates of Lagrange multipliers, is not directly available in subgradient schemes.

As a means for overcoming these weaknesses of subgradient optimization methods, we introduce the computation of an ergodic (averaged) sequence of subgradients. Specifically, we consider a nonsmooth, convex program solved by a conditional subgradient optimization scheme (of which the traditional subgradient optimization method is a special case) with divergent series step lengths, which generates a sequence of iterates that converges to an optimal solution. We show that the elements of the ergodic sequence of subgradients in the limit fulfill the optimality conditions at this optimal solution. Further, we use the convergence properties of the ergodic sequence of subgradients to establish convergence of an ergodic sequence of Lagrange multipliers. Finally, some potential applications of these ergodic results are briefly discussed.

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