Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distributionally robust possibilistic optimization problems   In this paper a class of optimization problems with uncertain linear
constraints is discussed. It is assumed that the constraint coefficients are
random vectors whose probability distributions are only partially known.
Possibility theory is used to model the imprecise probabilities. In one of the
interpretations, a possibility distribution (a membership function of a fuzzy
set) in the set of coefficient realizations induces a necessity measure, which
in turn defines a family of probability distributions in this set. The
distributionally robust approach is then used to transform the imprecise
constraints into deterministic counterparts. Namely, the uncertain left-had
side of each constraint is replaced with the expected value with respect to the
worst probability distribution that can occur. It is shown how to represent the
resulting problem by using linear or second order cone constraints. This leads
to problems which are computationally tractable for a wide class of
optimization models, in particular for linear programming.

15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc