Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Non-asymptotic confidence bounds for the optimal value of a stochastic
  program   We discuss a general approach to building non-asymptotic confidence bounds
for stochastic optimization problems. Our principal contribution is the
observation that a Sample Average Approximation of a problem supplies upper and
lower bounds for the optimal value of the problem which are essentially better
than the quality of the corresponding optimal solutions. At the same time, such
bounds are more reliable than "standard" confidence bounds obtained through the
asymptotic approach. We also discuss bounding the optimal value of MinMax
Stochastic Optimization and stochastically constrained problems. We conclude
with a simulation study illustrating the numerical behavior of the proposed
bounds.

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The categories of this paper are: quant-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-th
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The categories of this paper are: dg-ga
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The categories of this paper are: alg-geom
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The categories of this paper are: astro-ph
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The categories of this paper are: physics
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The categories of this paper are: q-alg
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The categories of this paper are: math-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: econ
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The categories of this paper are: stat
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The categories of this paper are: supr-con
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The categories of this paper are: cmp-lg
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The categories of this paper are: atom-ph