Joint Sparsity and Order Optimization Based on ADMM With Non-Uniform Group Hard Thresholding

Matsuoka, Ryota; Kyochi, Seisuke; Ono, Shunsuke; Okuda, Masahiro

doi:10.1109/tcsi.2017.2763969

Cited by 22 publications

(16 citation statements)

References 41 publications

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“…We used p = 0.05 and we started the optimization from initial filters obtained by Matlab function firpm with equally weighted passbands and stopbands. The last two columns in Table III show the comparison of our results and the results in [9]. We obtain the sparsity equal to that in [9] for four filters, worse for two and better for one filter.…”

Section: Examplementioning

confidence: 67%

“…The last two columns in Table III show the comparison of our results and the results in [9]. We obtain the sparsity equal to that in [9] for four filters, worse for two and better for one filter.…”

Section: Examplementioning

confidence: 67%

“…Our method does not support such a design. However, we found the specifications in [9] interesting because these filters have high initial orders, steep transition bands, and low required approximation errors.…”

Section: Examplementioning

confidence: 97%

“…The third example describes the design of various filters obtained using specifications in [9]. The paper referred to describes joint minimization of sparsity and filter order.…”

Section: Examplementioning

confidence: 99%

“…The first five columns in Table III contain the specifications of seven different filters from [9], based on which we perform our design. We used p = 0.05 and we started the optimization from initial filters obtained by Matlab function firpm with equally weighted passbands and stopbands.…”

Section: Examplementioning

confidence: 99%

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Sparse FIR Filter Design Based on Signomial Programming

Bellotti

Vučić

2020

ELEKTRON ELEKTROTECH

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The goal of sparse FIR filter design is to minimize the number of nonzero filter coefficients, while keeping its frequency response within specified boundaries. Such a design can be formally expressed via minimization of l0-norm of filter’s impulse response. Unfortunately, the corresponding minimization problem has combinatorial complexity. Therefore, many design methods are developed, which solve the problem approximately, or which solve the approximate problem exactly. In this paper, we propose an approach, which is based on the approximation of the l0-norm by an lp-norm with 0 < p < 1. We minimize the lp-norm using recently developed method for signomial programming (SGP). Our design starts with forming a SGP problem that describes filter specifications. The optimum solution of the problem is then found by using iterative procedure, which solves a geometric program in each iteration. The filters whose magnitude responses are constrained in minimax sense are considered. The design examples are provided illustrating that the proposed method, in most cases, results in filters with higher sparsity than those of the filters obtained by recently published methods.

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Section: Examplementioning

confidence: 67%

Section: Examplementioning

confidence: 67%

Section: Examplementioning

confidence: 97%

“…The third example describes the design of various filters obtained using specifications in [9]. The paper referred to describes joint minimization of sparsity and filter order.…”

Section: Examplementioning

confidence: 99%

Section: Examplementioning

confidence: 99%

See 3 more Smart Citations