In this paper we propose the Augmented-UCB (AugUCB) algorithm for a fixed-budget version of the thresholding bandit problem (TBP), where the objective is to identify a set of arms whose quality is above a threshold. A key feature of AugUCB is that it uses both mean and variance estimates to eliminate arms that have been sufficiently explored; to the best of our knowledge this is the first algorithm to employ such an approach for the considered TBP. Theoretically, we obtain an upper bound on the loss (probability of mis-classification) incurred by AugUCB. Although UCBEV in literature provides a better guarantee, it is important to emphasize that UCBEV has access to problem complexity (whose computation requires arms' mean and variances), and hence is not realistic in practice; this is in contrast to AugUCB whose implementation does not require any such complexity inputs. We conduct extensive simulation experiments to validate the performance of AugUCB. Through our simulation work, we establish that AugUCB, owing to its utilization of variance estimates, performs significantly better than the state-of-the-art APT, CSAR and other non variance-based algorithms.
The theory and mathematical bases of a-posteriori error estimates are explained. It is shown that the Medial Axis of a body can be used to decompose it into a set of mutually non-overlapping quadrilateral and triangular primitives. A mesh generation scheme used to generate quadrilaterals inside these primitives is also presented together with its relevant implementation aspects. A new h-refinement strategy based on weighted average energy norm and enhanced by strain energy density ratios is proposed and two typical problems are solved to demonstrate its efficiency over the conventional refinement strategy in the relative improvement of global asymptotic convergence.
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