Robust optimization in spline regression models for multi-model regulatory networks under polyhedral uncertainty

Özmen A., Kropat E., Weber G.

OPTIMIZATION, vol.66, no.12, pp.2135-2155, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 66 Issue: 12
  • Publication Date: 2017
  • Doi Number: 10.1080/02331934.2016.1209672
  • Journal Name: OPTIMIZATION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus
  • Page Numbers: pp.2135-2155
  • Keywords: Regulatory networks, robust optimization, polyhedral uncertainty, conic quadratic programming, RCMARS, MARKET, POWER, CAPACITY, PRICE
  • TED University Affiliated: No


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.