We investigate risk-averse stochastic optimization problems with a risk-shaping constraint in the form of a stochastic-order relation. Both univariate and multivariate orders are considered. We extend ...
A framework based on advanced AI techniques can solve complex, computationally intensive problems faster and in a more more scalable way than state-of-the-art methods, according to a new study. A ...
The leading approach to the simplex method, a widely used technique for balancing complex logistical constraints, can’t get ...
SIAM Journal on Numerical Analysis, Vol. 50, No. 1 (2012), pp. 328-353 (26 pages) In this article we construct and analyze multigrid preconditioned for discretizations of operators of the form D λ + K ...
Traffic modeling has been of interest to mathematicians since the 1950s. Research in the area has only grown as road traffic control presents an ever-increasing problem. In a new paper, authors ...
There’s a lot of excitement around exascale-class supercomputing and the possibilities of quantum computing, but there’s an emerging alternative advanced computing paradigm that transcends the limits ...
where \(\mathsf{G}(\cdot)\) is some convex operator and \(\mathcal{F}\) is as set of feasible input distributions. Examples of such an optimization problem include finding capacity in information ...
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