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W2802077215 abstract "An improved nonconvex optimization based algorithm for the reconstruction of sparse signals for compressive sensing is proposed. The algorithm is based on minimizing nonconvex Lp pseudonorm by using two multiple sequences of sub-optimizations. If the two iterates in between any two sub-optimizations happen to be very similar, one of the iterates is merged with the other. The merged iterate is then set to another randomly chosen vector that is orthogonal to the iterate obtained after merging. The multiple sequences of sub-optimizations can be implemented to run simultaneously in two cores of a multicore computer. Simulation results are presented which indicate that the proposed algorithm can offer improvement in the percentage of perfect signal reconstructions by upto 7% and reduction in the CPU time required to run the algorithm by upto 11%, relative to the competing algorithms." @default.
W2802077215 created "2018-05-17" @default.
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W2802077215 date "2018-01-01" @default.
W2802077215 modified "2023-09-27" @default.
W2802077215 title "Sparse Signal Reconstruction Using Multi-Sequential Lp Optimization" @default.
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W2802077215 doi "https://doi.org/10.1109/iscas.2018.8351059" @default.
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