期刊
OPTIMIZATION
卷 66, 期 12, 页码 2135-2155出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/02331934.2016.1209672
关键词
Regulatory networks; robust optimization; polyhedral uncertainty; conic quadratic programming; RCMARS
In our study, we integrate the data uncertainty of real-world models into our regulatory systems and robustify them. We newly introduce and analyse robust time-discrete target-environment regulatory systems under polyhedral uncertainty through robust optimization. Robust optimization has reached a great importance as a modelling framework for immunizing against parametric uncertainties and the integration of uncertain data is of considerable importance for the model's reliability of a highly interconnected system. Then, we present a numerical example to demonstrate the efficiency of our new robust regression method for regulatory networks. The results indicate that our approach can successfully approximate the target-environment interaction, based on the expression values of all targets and environmental factors.
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