Enhancement of spin mixing conductance in La_0.7Sr_0.3MnO₃/LaNiO₃/SrRuO₃ heterostructures

Spin pumping and the effective spin-mixing conductance in heterostructures based on magnetic oxide trilayers composed of La0.7Sr0.3MnO3 (LSMO), LaNiO3 (LNO), and SrRuO3 (SRO) are investigated. The heterostructures serve as a model system for an estimation of the effective spin-mixing conductance at the different interfaces. The results show that by introducing a LNO interlayer between LSMO and SRO, the total effective spin-mixing conductance increases due to the much more favorable interface of LSMO/LNO with respect to the LSMO/SRO interface. Nevertheless, the spin current into the SRO does not decrease because of the spin diffusion length of λLNO ≈ 3.2nm in the LNO. This value is two times higher than that of SRO. The results show the potential of using oxide interfaces to tune the effective spin-mixing conductance in heterostructures and to bring novel functionalities into spintronics by implementing complex oxides

Comparison of some Reduced Representation Approximations

In the field of numerical approximation, specialists considering highly complex problems have recently proposed various ways to simplify their underlying problems. In this field, depending on the problem they were tackling and the community that are at work, different approaches have been developed with some success and have even gained some maturity, the applications can now be applied to information analysis or for numerical simulation of PDE's. At this point, a crossed analysis and effort for understanding the similarities and the differences between these approaches that found their starting points in different backgrounds is of interest. It is the purpose of this paper to contribute to this effort by comparing some constructive reduced representations of complex functions. We present here in full details the Adaptive Cross Approximation (ACA) and the Empirical Interpolation Method (EIM) together with other approaches that enter in the same category

Geometric methods on low-rank matrix and tensor manifolds

In this chapter we present numerical methods for low-rank matrix and tensor problems that explicitly make use of the geometry of rank constrained matrix and tensor spaces. We focus on two types of problems: The first are optimization problems, like matrix and tensor completion, solving linear systems and eigenvalue problems. Such problems can be solved by numerical optimization for manifolds, called Riemannian optimization methods. We will explain the basic elements of differential geometry in order to apply such methods efficiently to rank constrained matrix and tensor spaces. The second type of problem is ordinary differential equations, defined on matrix and tensor spaces. We show how their solution can be approximated by the dynamical low-rank principle, and discuss several numerical integrators that rely in an essential way on geometric properties that are characteristic to sets of low rank matrices and tensors