The method of recovering a low-rank matrix with an unknown fraction whose entries are arbitrarily corrupted is known as the robust principal component analysis （RPCA）. This RPCA problem, under some conditions, can be exactly solved via convex optimization by minimizing a combination of the nuclear norm and the 11 norm. In this paper, an algorithm based on the Douglas-Rachford splitting method is proposed for solving the RPCA problem. First, the convex optimization problem is solved by canceling the constraint of the variables, and ~hen the proximity operators of the objective function are computed alternately. The new algorithm can exactly recover the low-rank and sparse components simultaneously, and it is proved to be convergent. Numerical simulations demonstrate the practical utility of the proposed algorithm.
Aiming at the isoparametric bilinear finite volume element scheme,we initially derive an asymptotic expansion and a high accuracy combination formula of the derivatives in the sense of pointwise by employing the energy-embedded method on uniform grids.Furthermore,we prove that the approximate derivatives are convergent of order two.Finally,numerical examples verify the theoretical results.
A new first-order optimality condition for the basis pursuit denoise （BPDN） problem is derived. This condition provides a new approach to choose the penalty param- eters adaptively for a fixed point iteration algorithm. Meanwhile, the result is extended to matrix completion which is a new field on the heel of the compressed sensing. The numerical experiments of sparse vector recovery and low-rank matrix completion show validity of the theoretic results.
In this paper,we propose a fast proximity point algorithm and apply it to total variation(TV)based image restoration.The novel method is derived from the idea of establishing a general proximity point operator framework based on which new first-order schemes for total variation(TV)based image restoration have been proposed.Many current algorithms for TV-based image restoration,such as Chambolle’s projection algorithm,the split Bregman algorithm,the Berm´udez-Moreno algorithm,the Jia-Zhao denoising algorithm,and the fixed point algorithm,can be viewed as special cases of the new first-order schemes.Moreover,the convergence of the new algorithm has been analyzed at length.Finally,we make comparisons with the split Bregman algorithm which is one of the best algorithms for solving TV-based image restoration at present.Numerical experiments illustrate the efficiency of the proposed algorithms.
In present paper,the locomotion of an oblate jellyfish is numerically investigated by using a momentum exchange-based immersed boundary-Lattice Boltzmann method based on a dynamic model describing the oblate jellyfish.The present investigation is agreed fairly well with the previous experimental works.The Reynolds number and the mass density of the jellyfish are found to have significant effects on the locomotion of the oblate jellyfish.Increasing Reynolds number,the motion frequency of the jellyfish becomes slow due to the reduced work done for the pulsations,and decreases and increases before and after the mass density ratio of the jellyfish to the carried fluid is 0.1.The total work increases rapidly at small mass density ratios and slowly increases to a constant value at large mass density ratio.Moreover,as mass density ratio increases,the maximum forward velocity significantly reduces in the contraction stage,while the minimum forward velocity increases in the relaxation stage.
Hai-Zhuan YuanShi ShuXiao-Dong NiuMingjun LiYang Hu
