Alexandru Onose

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The Square Kilometre Array will be the largest sensor ever built by humankind and will acquire unprecedented amounts of data in radio astronomy. Recently, convex opti-misation algorithms coupled with sparsity priors have been shown to outperform traditional radio interferometric imaging methods such as clean. Thus, it is of paramount importance to extend(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)
Based on the iterated cyclic adaptive matching pursuit algorithm , we construct a low complexity approximate variant for finding sparse solutions to systems of linear equations. We employ a greedy neighbor permutation strategy coupled with an approximate scalar product matrix to ensure that the complexity of the algorithm remains low. The sparse solution is(More)