Alexandru Onose

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In the context of next generation radio telescopes, like the Square Kilometre Array , the efficient processing of large-scale datasets is extremely important. Convex optimisation tasks under the compressive sensing framework have recently emerged and provide both enhanced image reconstruction quality and scalability to increasingly larger data sets. We(More)
—Next generation radio telescopes, like the Square Kilometre Array, will acquire an unprecedented amount of data for radio astronomy. The development of fast, parallelisable or distributed algorithms for handling such large-scale data sets is of prime importance. Motivated by this, we investigate herein a convex optimisation algorithmic structure, based on(More)
Randomized coordinate descent (RCD), attractive for its ro-bustness and ability to cope with large scale problems, is here investigated for the first time in an adaptive context. We present an RCD adaptive algorithm for finding sparse least-squares solutions to linear systems, in particular for FIR channel identification. The algorithm has low and tunable(More)