this paper gives a new application of dea to evaluate the scheduling solutions of parallel processing. it evaluates the scheduling solutions of parallel processing using the non-convex dea model, fdh model. by introducing each solution of parallel processing scheduling as a dmu with some relevant inputs and outputs this paper shows that how the most efficient schedule(s) can be identified.

Let $ H$ be a Hilbert space and $C$ be a closed, convex and nonempty subset of $H$. Let $T:C rightarrow H$ be a non-self and non-expansive mapping. V. Colao and G. Marino with particular choice of the sequence  ${alpha_{n}}$ in Krasonselskii-Mann algorithm, ${x}_{n+1}={alpha}_{n}{x}_{n}+(1-{alpha}_{n})T({x}_{n}),$ proved both weak and strong converging results. In this paper, we generalize thei...

This paper gives a new application of DEA to evaluate the scheduling solutions of parallel processing. It evaluates the scheduling solutions of parallel processing using the non-convex DEA model, FDH model. By introducing each solution of parallel processing scheduling as a DMU with some relevant inputs and outputs this paper shows that how the most efficient schedule(s) can be identified.

We study weakly compact operators from a C∗-algebra with values in a complete locally convex space. They constitute a natural non-commutative generalization of finitely additive vector measures with values in a locally convex space. Several results of Brooks, Sâıto and Wright are extended to this more general setting. Building on an approach due to Sâıto and Wright, we obtain our theorems on no...

We propose a new method for robust PCA – the task of recovering a low-rank matrix from sparse corruptions that are of unknown value and support. Our method involves alternating between projecting appropriate residuals onto the set of lowrank matrices, and the set of sparse matrices; each projection is non-convex but easy to compute. In spite of this non-convexity, we establish exact recovery of...