[arXiv] BigDataFr recommends: A Flexible Coordinate Descent Method for Big Data Applications #datascientist #machinelearning

BigDatafr recommends: A Flexible Coordinate Descent Method for Big Data Applications ‘In this paper we present a novel randomized block coordinate descent method for the minimization of a convex composite objective function. The method uses (approximate) partial second-order (curvature) information, so that the algorithm performance is more robust when applied to highly nonseparable or ill […]

[arXiv] BigDataFr recommends:A Flexible Coordinate Descent Method for Big Data Applications

BigDataFr recommends: A Flexible Coordinate Descent Method for Big Data Applications ‘In this paper we present a novel randomized block coordinate descent method for the minimization of a convex composite objective function. The method uses (approximate) partial second-order (curvature) information, so that the algorithm performance is more robust when applied to highly nonseparable or ill […]

[arXiv] BigDataFr recommends: Experimental Study of the Cloud Architecture Selection for Effective Big Data Processing

BigDataFr recommends: Experimental Study of the Cloud Architecture Selection for Effective Big Data Processing ‘Big data dictate their requirements to the hardware and software. Simple migration to the cloud data processing, while solving the problem of increasing computational capabilities, however creates some issues: the need to ensure the safety, the need to control the quality […]