In this paper, we call to bridge the gap between what makes highly-concurrent optimistic algorithms work and current approaches for proving their correctness. The Problem: Verification of Optimistic Concurrent Algorithms Highly-concurrent optimistic algorithms are notoriously hard to verify. In particular, verifying that an optimistic algorithm is linearizable [3] is quite challenging. (See, e....

In this paper, we prove an exponential rate of convergence result for a common estimator of conditional value-at-risk for bounded random variables. The bound on optimistic deviations is tighter while the bound on pessimistic deviations is more general and applies to a broader class of convex risk measures. © 2007 Elsevier B.V. All rights reserved.

in this paper, it is assumed that the “decision making units“( ) are consist of positive and negative input and output. firstly, the optimistic and pessimistic models have been suggested by using negative data and then units with most productive scale size are measured in optimistic and pessimistic models. these productive values are compared with double frontiers and hurwicz’s criterion to obt...