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We consider the linearly constrained separable convex minimization model, whose objective function is the sum of three convex functions without coupled variables. The generalized alternating direction method of multipliers (ADMM) is a very effective approach for solving this kind of problem. Recently, the literature of ADMM focus on three or more blocks. [14] has shown a global linear convergence...
In this paper, transform domain LMS (TDLMS) and TDLMS based on decomposition technology (TDLMS-DT) are mixed together by so-called convex combination approach to achieve relatively fast convergence speed and low steady-state performance. The simulation results confirm the efficiency of the proposed algorithm.
In this paper, we present an efficient algorithm for sparse signal recovery with high exact recovery rate. The main idea of the algorithm is to combine two existing methods: linearized Bregman algorithm and reweighting technique. Compared with other available methods, such as reweighted Basis Pursuit (BP) and linearized Bregman, the proposed algorithm has a much lower computational complexity with...
The research with respect to Particle Swarm Optimization is concentrated in improving their performance on avoiding local maxima. Since standard Particle Swarm Optimization does not perform well in many cases, we propose double chaotic particle swarm optimization algorithm based on logistic map. This chaotic movement has good randomness and ergodic statistics property of chaos sequence. We propose...
Robust pole assignment problem for linear systems with uncertainty is studied in this paper. The proposed gradient flow optimization algorithm is used to solve the Sylvester equations, in order that the close-loop control systems have the desired robust poles, namely, the uniformly asymptotically stable performance. The feedback gain matrix of the synthesized system can be derived from the gradient...
This paper studies an enhanced robust kernel least mean square (KLMS) adaptive filtering algorithm for nonlinear acoustic echo cancellation (NLAEC) in impulsive noise environment. Robust KLMS algorithm based on M-estimate theory shows robustness to simulated, Contaminated Gaussian (CG) impulsive noise. However, it fails to combat real-world impulsive noise which normally consists of a few consecutive...
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