A fast algorithm for the optimal design of high accuracy windows in signal processing

Yiu, Ka Fai Cedric; Gao, Ming; Shiu, Ting-Jang; Wu, Soon‐Yi; Tran, To; Claesson, Ingvar

doi:10.1080/10556788.2012.681659

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…Selected applications of LSIO to di¤erent …elds can be found in [4], [5], [20], [24], [25], [26], [58], [65], [80], [82], [119], [132], [134], [138], [139], [154], [161], [168], [169], [179], [184], [189], [194], [206], [205], [219], [220], [221] and [226].…”

Section: -Other Referencesmentioning

confidence: 99%

Recent contributions to linear semi-infinite optimization: an update

Goberna

López

2018

Ann Oper Res

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This paper reviews the state-of-the-art in the theory of deterministic and uncertain linear semi-in…nite optimization, presents some numerical approaches to this type of problems, and describes a selection of recent applications in a variety of …elds. Extensions to related optimization areas, as convex semi-in…nite optimization, linear in…nite optimization, and multi-objective linear semi-in…nite optimization, are also commented.

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Section: -Other Referencesmentioning

confidence: 99%

Recent contributions to linear semi-infinite optimization: an update

Goberna

López

2018

Ann Oper Res

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“…The convergence results of this exchange algorithm can be found in [17]. Then, we can apply Algorithm 1 to solve Problem (4.1).…”

Section: Problem Transformationmentioning

confidence: 99%

Design of Sparse Filters by a Discrete Filled Function Technique

Feng

Yiu

2018

Circuits Syst Signal Process

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In this paper, we consider the sparse filter design problem where some of the coefficients can be reduced to zeroes in order to lower implementation complexity. The objective is to choose the fewest number of nonzero filter coefficients to meet a given performance requirement. We formulate a discrete optimization problem to minimize the number of nonzero terms and develop a discrete search method to find the minimal nonzero terms. In each step, we need to consider a subproblem to design the filter coefficients with a given set of nonzero terms. We formulate this subproblem as a linear programming problem and apply an exchange algorithm to find the optimal coefficients. For illustration, we compare the proposed algorithm with existing methods and show that the proposed method gives better results in all our test cases. KeywordsSparse filter design • filled function • discrete search method 1 Introduction Digital finite-impulse response (FIR) filters design have been widely studied in the literature. There are many optimization methods available to deal with this class of problems. In particular, Parks-McClellan method is recognized to

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“…Selected applications of LSIO to di¤erent …elds can be found in [3], [4], [19], [20], [21], [49], [56], [69], [115], [117], [119], [120], [134], [140], [159], [164], [168], [180], [179], [191], [192] and [196].…”

Section: -Other Referencesmentioning

confidence: 99%