SNSF Consolidator Grant awarded to Prof. Daniel Kuhn
© 2015 Prof. Kuhn
Prof. Daniel Kuhn has been awarded a CONSOLIDATOR GRANT by the SNSF in the context of the so-called “Temporary Backup Schemes” for the European Research Council (ERC) grants. This transitional measure of the SNSF offers the awardee an adequate substitute for ERC grants.
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Optimization4U - Optimization under Unknown or Uncertain Uncertainty
Uncertainty is traditionally modeled via probability distributions. However, observable statistical data can often be explained by many strikingly different distributions. This "uncertainty about the uncertainty" poses a major challenge for optimization problems with uncertain parameters: estimation errors in the parameters' distribution are amplified through the optimization process and lead to biased (overly optimistic) optimization results as well as post-decision disappointment in out-of-sample tests.
The emerging field of distributionally robust optimization (DRO) seeks new optimization models whose solutions are optimized against all distributions consistent with the given prior information. Recent breakthrough work has shown that many DRO models can be solved in polynomial time even when the corresponding stochastic models are intractable. DRO models also offer a more realistic account of uncertainty and mitigate the post-decision disappointment characteristic of stochastic models.
This project will advance DRO as a dominant modeling paradigm for optimization under uncertainty and aims to lay the foundations for industry-size applications. We endeavor to make progress along four directions. (i) Distributions: We will use Choquet theory to significantly extend the class of distribution families that can be handled in a DRO framework. (ii) Dynamics: We will leverage modern decision rule techniques to solve dynamic DRO models without incurring a curse of dimensionality. (iii) Decision Support: We plan to develop a tailor-made modeling language and open-source software to make DRO methods accessible to decision-makers without expert knowledge in optimization. (iv) Dimensionality: As a guiding principle, we will use tractable conservative approximations whenever necessary to guarantee that all emerging DRO models remain polynomial-time solvable and thus have the potential to scale to industrially relevant problem sizes.
Duration of the project: 60 months
Total amount: CHF 1'069'840.-
Domains: Mathematics, Natural Sciences, Science of Management