Distributionally robust optimization with polynomial densities: Theory, models and algorithms

Etienne de Klerk, Daniel Kuhn, K.S. Postek

Research output: Contribution to journalArticleScientificpeer-review

11 Citations (Scopus)
16 Downloads (Pure)


In distributionally robust optimization the probability distribution of the uncertain problem parameters is itself uncertain, and a fictitious adversary, e.g., nature, chooses the worst distribution from within a known ambiguity set. A common shortcoming of most existing distributionally robust optimization models is that their ambiguity sets contain pathological discrete distributions that give nature too much freedom to inflict damage. We thus introduce a new class of ambiguity sets that contain only distributions with sum-of-squares (SOS) polynomial density functions of known degrees. We show that these ambiguity sets are highly expressive as they conveniently accommodate distributional information about higher-order moments, conditional probabilities, conditional moments or marginal distributions. Exploiting the theoretical properties of a measure-based hierarchy for polynomial optimization due to Lasserre (SIAM J Optim 21(3):864–885, 2011), we prove that certain worst-case expectation constraints are polynomial-time solvable under these new ambiguity sets. We also show how SOS densities can be used to approximately solve the general problem of moments. We showcase the applicability of the proposed approach in the context of a stylized portfolio optimization problem and a risk aggregation problem of an insurance company.
Original languageEnglish
Pages (from-to)265–296
JournalMathematical Programming
Issue number2
Publication statusPublished - Jun 2020


  • distributionally robust optimization
  • semidefinite programming
  • sum-of-squares polynomials
  • generalized eigenvalue problem


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