A Parallel optimization Approach on the Infinity Norm Minimization Problem

2019 ◽  
Author(s):  
Tianyi Liu ◽  
Minh Trinh Hoang ◽  
Yang Yang ◽  
Marius Pesavento
Keyword(s):  
Minimization Problem ◽  
Parallel Optimization ◽  
Optimization Approach ◽  
Infinity Norm ◽  
Norm Minimization

A new piecewise quadratic approximation approach for L0 norm minimization problem

2018 ◽  
Vol 62 (1) ◽  
pp. 185-204 ◽  
Author(s):  
Qian Li ◽  
Yanqin Bai ◽  
Changjun Yu ◽  
Ya-xiang Yuan
Keyword(s):  
Minimization Problem ◽  
Quadratic Approximation ◽  
Approximation Approach ◽  
Norm Minimization

Multiobjective approach developed for optimizing the dynamic behavior of incremental linear actuators

2014 ◽  
Vol 33 (3) ◽  
pp. 953-964 ◽  
Author(s):  
Imen Amdouni ◽  
Lilia El Amraoui ◽  
Frédéric Gillon ◽  
Mohamed Benrejeb ◽  
Pascal Brochet
Keyword(s):  
Multiobjective Optimization ◽  
Dynamic Model ◽  
Dynamic Behavior ◽  
Pareto Front ◽  
Computing Time ◽  
Design Parameters ◽  
Parallel Optimization ◽  
Optimization Approach ◽  
Content Type ◽  
Linear Actuators

Purpose – The purpose of this paper is to develop an optimal approach for optimizing the dynamic behavior of incremental linear actuators. Design/methodology/approach – First, a parameterized design model is built. Second, a dynamic model is implemented. This model takes into account the thrust force computed from a finite element model. Finally, the multiobjective optimization approach is applied to the dynamic model to optimize control as well as design parameters. Findings – The Pareto front resulting from the optimization approach (or the parallel optimization approach,) is better than the Pareto, which is obtained from the only application of MultiObjective Genetic Algorithm (MOGA) method (or parallel MOGA with the same number of optimization approach objective function evaluations). The only use of MOGA can reach the region near an optimal Pareto front, but it consumes more computing time than the multiobjective optimization approach. At each flowchart stage, parallelization leads to a significant reduction of computing time which is halved when using two-core machine. Originality/value – In order to solve the multiobjective problem, a hybrid algorithm based on MOGA is developed.

scholarly journals Robust Localization in Distributed MIMO Radar Using Delay and Angle Measurements with Impulsive Noise Robust TD/AOA Localization in Impulsive Noise

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Genling Huang ◽  
Yanlong Zhu
Keyword(s):  
Minimization Problem ◽  
Impulsive Noise ◽  
Target Position ◽  
Mimo Radar ◽  
Optimal Choice ◽  
Position Estimation ◽  
Localization Algorithm ◽  
Angle Of Arrival ◽  
Norm Minimization ◽  
Measurement Equations

This paper considers target localization using time delay (TD) and angle of arrival (AOA) measurements in distributed multiple-input multiple-output (MIMO) radar. Aiming at the problem that the localization performance of existing algorithms degrades sharply in the presence of impulsive noise, we propose a novel localization algorithm based on ℓ p -norm minimization and iteratively reweighted least squares (IRLS). Firstly, the TD and AOA measurement equations are established in the presence of zero-mean symmetric α-stable noise; then, the localization problem is transformed to a ℓ p -norm minimization problem by linearizing the measurement equations; and finally, the ℓ p -norm minimization problem is solved using IRLS by which the target position estimate is obtained, and the optimal choice of norm order p is deduced. Moreover, the Cramér–Rao bound (CRB) for target position estimation in impulsive noise is also derived, generalizing the Gaussian CRB. Simulation results demonstrate that the proposed algorithm outperforms existing algorithms in terms of localization accuracy and robustness in impulsive noise.

Line survey joint denoising via low-rank minimization

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. V21-V32 ◽  
Author(s):  
Zhao Liu ◽  
Jianwei Ma ◽  
Xueshan Yong
Keyword(s):  
Minimization Problem ◽  
Nuclear Norm ◽  
Low Rank ◽  
Single Shot ◽  
Rank Minimization ◽  
Nuclear Norm Minimization ◽  
Norm Minimization ◽  
Rank Matrix ◽  
Line Survey ◽  
Low Rank Matrix

Prestack seismic data denoising is an important step in seismic processing due to the development of prestack time migration. Reduced-rank filtering is a state-of-the-art method for prestack seismic denoising that uses predictability between neighbor traces for each single frequency. Different from the original way of embedding low-rank matrix based on the Hankel or Toeplitz transform, we have developed a new multishot gathers joint denoising method in a line survey, which used a new way of rearranging data to a matrix with low rank. Inspired by video denoising, each single-shot record in the line survey can be viewed as a frame in the video sequence. Due to high redundancy and similar event structure among the shot gathers, similar patches can be selected from different shot gathers in the line survey to rearrange a low-rank matrix. Then, seismic denoising is formulated into a low-rank minimization problem that can be further relaxed into a nuclear-norm minimization problem. A fast algorithm, called the orthogonal rank-one matrix pursuit, is used to solve the nuclear-norm minimization. Using this method avoids the computation of a full singular value decomposition. Our method is validated using synthetic and field data, in comparison with [Formula: see text] deconvolution and singular spectrum analysis methods.

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