An Adaptive Convex Combination between Prediction and Correction in Online Quadratic optimization

Conference paper (2021)

Authors

E. Benenati Team Sergio Grammatico - Mechanical, Maritime and Materials Engineering
S. Grammatico Team Sergio Grammatico - Mechanical, Maritime and Materials Engineering ,
Research Group
Team Sergio Grammatico (Mechanical, Maritime and Materials Engineering) (TU Delft)
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Published Date

2021

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Sergio Grammatico

Abstract

We study a particular class of online quadratic optimization problems, where the objective function linearly depends on some time-varying parameters. In the context of prediction-correction algorithms, that is, algorithms that combine a prediction of the future cost function and a correction on the observation of the past one, we explore the effect of a stochastic disturbance in the prediction. We then propose an algorithm that leverages the information on the prediction uncertainty and on the problem structure to approximate the optimal combination between prediction and correction.

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