Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Conic Reformulations for Kullback-Leibler Divergence Constrained
  Distributionally Robust Optimization and Applications   In this paper, we consider a distributionally robust optimization (DRO) model
in which the ambiguity set is defined as the set of distributions whose
Kullback-Leibler (KL) divergence to an empirical distribution is bounded.
Utilizing the fact that KL divergence is an exponential cone representable
function, we obtain the robust counterpart of the KL divergence constrained DRO
problem as a dual exponential cone constrained program under mild assumptions
on the underlying optimization problem. The resulting conic reformulation of
the original optimization problem can be directly solved by a commercial conic
programming solver. We specialize our generic formulation to two classical
optimization problems, namely, the Newsvendor Problem and the Uncapacitated
Facility Location Problem. Our computational study in an out-of-sample analysis
shows that the solutions obtained via the DRO approach yield significantly
better performance in terms of the dispersion of the cost realizations while
the central tendency deteriorates only slightly compared to the solutions
obtained by stochastic programming.

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The categories of this paper are: ao-sci
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The categories of this paper are: atom-ph
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The categories of this paper are: q-bio
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: solv-int
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The categories of this paper are: supr-con
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The categories of this paper are: hep-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: comp-gas
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The categories of this paper are: eess
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The categories of this paper are: patt-sol
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-th
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The categories of this paper are: chem-ph
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The categories of this paper are: physics