3 resultados para Autler-Townes splitting


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In this paper we introduce four scenario Cluster based Lagrangian Decomposition (CLD) procedures for obtaining strong lower bounds to the (optimal) solution value of two-stage stochastic mixed 0-1 problems. At each iteration of the Lagrangian based procedures, the traditional aim consists of obtaining the solution value of the corresponding Lagrangian dual via solving scenario submodels once the nonanticipativity constraints have been dualized. Instead of considering a splitting variable representation over the set of scenarios, we propose to decompose the model into a set of scenario clusters. We compare the computational performance of the four Lagrange multiplier updating procedures, namely the Subgradient Method, the Volume Algorithm, the Progressive Hedging Algorithm and the Dynamic Constrained Cutting Plane scheme for different numbers of scenario clusters and different dimensions of the original problem. Our computational experience shows that the CLD bound and its computational effort depend on the number of scenario clusters to consider. In any case, our results show that the CLD procedures outperform the traditional LD scheme for single scenarios both in the quality of the bounds and computational effort. All the procedures have been implemented in a C++ experimental code. A broad computational experience is reported on a test of randomly generated instances by using the MIP solvers COIN-OR and CPLEX for the auxiliary mixed 0-1 cluster submodels, this last solver within the open source engine COIN-OR. We also give computational evidence of the model tightening effect that the preprocessing techniques, cut generation and appending and parallel computing tools have in stochastic integer optimization. Finally, we have observed that the plain use of both solvers does not provide the optimal solution of the instances included in the testbed with which we have experimented but for two toy instances in affordable elapsed time. On the other hand the proposed procedures provide strong lower bounds (or the same solution value) in a considerably shorter elapsed time for the quasi-optimal solution obtained by other means for the original stochastic problem.

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Spintronics, or spin electronics, is aimed at efficient control and manipulation of spin degrees of freedom in electron systems. To comply with demands of nowaday spintronics, the studies of electron systems hosting giant spin-orbit-split electron states have become one of the most important problems providing us with a basis for desirable spintronics devices. In construction of such devices, it is also tempting to involve graphene, which has attracted great attention because of its unique and remarkable electronic properties and was recognized as a viable replacement for silicon in electronics. In this case, a challenging goal is to lift spin degeneracy of graphene Dirac states. Here, we propose a novel pathway to achieve this goal by means of coupling of graphene and polar-substrate surface states with giant Rashba-type spin-splitting. We theoretically demonstrate it by constructing the graphene@BiTeCl system, which appears to possess spin-helical graphene Dirac states caused by the strong interaction of Dirac and Rashba electrons. We anticipate that our findings will stimulate rapid growth in theoretical and experimental investigations of graphene Dirac states with real spin-momentum locking, which can revolutionize the graphene spintronics and become a reliable base for prospective spintronics applications.

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The surface electronic structure of the narrow-gap seminconductor BiTeI exhibits a large Rashba-splitting which strongly depends on the surface termination. Here we report on a detailed investigation of the surface morphology and electronic properties of cleaved BiTeI single crystals by scanning tunneling microscopy, photoelectron spectroscopy (ARPES, XPS), electron diffraction (SPA-LEED) and density functional theory calculations. Our measurements confirm a previously reported coexistence of Te- and I-terminated surface areas