Optimization Filters for Stochastic Time-Varying Convex Optimization

We look at a stochastic time-varying optimization problem and we formulate online algorithms to find and track its optimizers in expectation. The algorithms are derived from the intuition that standard prediction and correction steps can be seen as a dynamical system and a measurement equation, resp...

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Bibliographic Details
Published in2023 European Control Conference (ECC) pp. 1 - 6
Main Authors Simonetto, Andrea, Massioni, Paolo
Format Conference Proceeding
LanguageEnglish
Published EUCA 13.06.2023
Subjects
Europe
Filtering algorithms
Heuristic algorithms
Kalman filters
Linear matrix inequalities
Noise measurement
Prediction algorithms
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Summary:We look at a stochastic time-varying optimization problem and we formulate online algorithms to find and track its optimizers in expectation. The algorithms are derived from the intuition that standard prediction and correction steps can be seen as a dynamical system and a measurement equation, respectively, yielding the notion of filter design. The optimization algorithms are then based on an extended Kalman filter in the unconstrained case, and on a linear matrix inequality condition in the constrained case. Some special cases and variations are discussed, and supporting numerical results are presented from real data sets in ride-hailing scenarios. The results are encouraging, especially when predictions are accurate, a case which is often encountered in practice when historical data is abundant
DOI:10.23919/ECC57647.2023.10178237