Adaptive Sequential Stochastic Optimization

A framework is introduced for sequentially solving convex stochastic minimization problems, where the objective functions change slowly, in the sense that the distance between successive minimizers is bounded. The minimization problems are solved by sequentially applying a selected optimization algo...

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Veröffentlicht in:	IEEE transactions on automatic control 2019-02, Vol.64 (2), p.496-509
Hauptverfasser:	Wilson, Craig, Veeravalli, Venugopal V., Nedic, Angelia
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Approximation algorithms Bayes methods Computer simulation Convex functions Criteria Gradient methods Linear programming Minimization Optimization Optimization algorithms stochastic optimization time-varying objective Tracking tracking problems Upper bound Upper bounds
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