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

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

Conference Paper (2021)

Author(s)

Emilio Benenati (TU Delft - Mechanical Engineering)

Sergio Grammatico (TU Delft - Mechanical Engineering, TU Delft - Mechanical Engineering)

Research Group

Team Sergio Grammatico

Optimization Uncertainty Quadratic programming

DOI related publication

https://doi.org/10.1109/ICSTCC52150.2021.9607070 Final published version

To reference this document use

https://resolver.tudelft.nl/uuid:67f54652-016a-4933-b270-f61a5ae03beb

More Info

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Publication Year

2021

Language

English

Research Group

Team Sergio Grammatico

Pages (from-to)

138-143

ISBN (electronic)

978-1-6654-1496-8

Event

25th International Conference on System Theory, Control and Computing, ICSTCC 2021 (2021-10-20 - 2021-10-23), Iasi, Romania

Downloads counter

123

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.