Abstract. We present an algorithmic technique based on stochastic ordering to obtain upper and lower bounding distributions for the results of some optimisation ...
We present an algorithmic technique based on stochastic ordering to obtain upper and lower bounding distributions for the results of some optimisation ...
We present an algorithmic technique based on stochastic ordering to obtain upper and lower bounding distributions for the results of some optimisation ...
The paper deals with the solution of a stochastic optimization problem under incomplete information. It is assumed that the distribution of probabilistic param- ...
We argue that two-stage (linear) stochastic program- ming problems with recourse can be solved with a reasonable accuracy by us- ing Monte Carlo sampling ...
In this work, we study the secure stochastic convex optimization problem, in which the learner aims to optimize the accuracy, i.e., obtain an accurate estimate ...
Oct 14, 2024 · Stochastic Rounding (SR) is a probabilistic rounding mode that is surprisingly effective in large-scale computations and low-precision arithmetic.
Abstract. We address the trade-off between the computational resources needed to process a large data set and the number of samples available from the data ...
Oct 9, 2004 · We argue that two-stage (linear) stochastic programming problems with recourse can be solved with a reasonable accuracy by using Monte Carlo ...
Our approach for obtaining sample complexity bounds for nonlinear dynamics relies on a linear, albeit infinite dimensional, representation of nonlinear dynamics ...
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