On Regularization Schemes for Data-Driven Optimization

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper presents several regularization schemes for data-driven optimization problems. Data-driven optimization is a challenging task because the model or the distribution describing the inherent stochastic disturbance is unknown but instead only a set of data sampled from it can be available. Most existing literature tackles the data-driven optimization problem by using the approach of distributionally robust optimization which is however an infinite-dimensional optimization problem and thus is required to be transformed into a tractable finite-dimensional optimization problem. As a different line, the regularization schemes presented in this paper contribute to solving the data-driven minimization problem by approximating the unknown expectation with some data-dependent surrogates in a high confidence and by minimizing the surrogates in a tractable way. The tradeoff between the confidence level and the optimization error is analyzed.

Original languageEnglish (US)
Title of host publicationProceedings of the 31st Chinese Control and Decision Conference, CCDC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781728101057
StatePublished - Jun 2019
Event31st Chinese Control and Decision Conference, CCDC 2019 - Nanchang, China
Duration: Jun 3 2019Jun 5 2019

Publication series

NameProceedings of the 31st Chinese Control and Decision Conference, CCDC 2019


Conference31st Chinese Control and Decision Conference, CCDC 2019


  • Cressie-Read Likelihood
  • Data-driven optimization
  • chi-distribution
  • distributionally robust optimization
  • empirical likelihood
  • phi-divergence

ASJC Scopus subject areas

  • General Decision Sciences
  • Control and Systems Engineering
  • Control and Optimization


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