Recently, Ramadan et al. have focused on the following matrix equation: A(1)V + A(2)(V) over bar + B1W + B-2(W) over bar = E1VF1+ E-2(V) over barF(2)+ C and propounded two gradient- based iterative algorithms for solv...
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Recently, Ramadan et al. have focused on the following matrix equation: A(1)V + A(2)(V) over bar + B1W + B-2(W) over bar = E1VF1+ E-2(V) over barF(2)+ C and propounded two gradient- based iterative algorithms for solving the above matrix equation over reflexive and Hermitian reflexive matrices, respectively. In this paper, we develop two new iterative algorithms based on a two- dimensional projection technique for solving the mentioned matrix equation over reflexive and Hermitian reflexive matrices. The performance of our proposed algorithms is collated with the gradient- based iterative algorithms. It is both theoretically and experimentally demonstrated that the approaches handled surpass the offered algorithms in the earlier referred work in solving the mentioned matrix equation over reflexive and Hermitian reflexive matrices. In addition, it is briefly discussed that a one- dimensional projection technique can accelerate the speed of convergence of the gradient- based iterative algorithm for solving general coupled Sylvester matrix equations over reflexive matrices without assuming the restriction of the existence of a unique solution.
In this article, we establish the existence of solution for two dimensional nonlinear fractional integral equation using fixed point theorem and measure of noncompactness. Applicability of our results is shown by some...
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In this article, we establish the existence of solution for two dimensional nonlinear fractional integral equation using fixed point theorem and measure of noncompactness. Applicability of our results is shown by some examples and for validity of the proposed method we make an iterative algorithm by semi-analytic technique that finds a closed form of the solution with an acceptable accuracy. Ability of the proposed method is granted by comparison with another method found in existing literature. (C) 2019 Elsevier B.V. All rights reserved.
This article introduces a new iterative technique for solving systems of linear equations of the kind Ax = b. Convergence, and with a given rate, is guaranteed with the square nonsingular matrix A being non-negative. ...
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This article introduces a new iterative technique for solving systems of linear equations of the kind Ax = b. Convergence, and with a given rate, is guaranteed with the square nonsingular matrix A being non-negative. The iterative algorithm depends on a scheme derived from Bayesian updating. The algorithm is shown to compare very favorably with the wisely used GMRES routine. With the algorithm being easy to code, it has the potential to be highly useable.
This paper presents an efficient iterative least-squares algorithm for phase-shifting interferometry. If the phase shifts contain errors, the residuals (i.e. the differences between the recorded intensities and their ...
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This paper presents an efficient iterative least-squares algorithm for phase-shifting interferometry. If the phase shifts contain errors, the residuals (i.e. the differences between the recorded intensities and their recalculated values) of least-squares algorithm are dependent on these errors. Based on this fact, an approach that allows estimating the phase shift errors from these residuals is derived. Using the results, the values of phase shifts are corrected. By iterating the two steps: phase evaluation and phase shift correction, the phase distribution can be accurately reconstructed. Its validity and performances have been investigated by both the numerical simulation and experiment results. The convergence condition with this algorithm is also discussed. (c) 2006 Elsevier Ltd. All rights reserved.
Diagonal dominance of matrices and M-matrices (and H-matrices) play an important role in stability theory of dynamical systems. But it is difficult to determine the scaling matrix G = diag(g(1),...,g(n)) (g(1),...,g(n...
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Diagonal dominance of matrices and M-matrices (and H-matrices) play an important role in stability theory of dynamical systems. But it is difficult to determine the scaling matrix G = diag(g(1),...,g(n)) (g(1),...,g(n) > 0) with AG being a strictly diagonally dominant matrix. In this paper, an convergent iterative algorithm for determine the scaling matrix for an irreducible M-matrix (H-matrix) A is presented. (c) 2004 Published by Elsevier Inc.
Performance Measure Approach (PMA) is an alternative way for evaluation of probabilistic constraints in reliability-based design optimization other than traditional Reliability Index Approach (RIA). In PMA, the probab...
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Performance Measure Approach (PMA) is an alternative way for evaluation of probabilistic constraints in reliability-based design optimization other than traditional Reliability Index Approach (RIA). In PMA, the probabilistic performance measure (PPM) is obtained through locating the minimum performance target point (MPTP) with the specified target reliability index in standard normal space, which is also called inverse reliability analysis. The advanced mean-value (AMV) method is well suitable for locating MPTP due to its simplicity and efficiency. However, AMV may have difficult to converge for highly nonlinear performance function. In this paper a step length adjustment (SLA) iterative algorithm, which introduces a "new" step length to control the convergence of the sequence, is proposed. This step length is new because the line search process for step length selection is not needed and it may be constant during the whole iteration process or decrease successively several times using a self-adjust strategy. It is proved that the AMV method is a special case of the SLA algorithm when the step length tends to infinity and the reason why AMV diverges is illustrated. SLA is as simple as AMV and does not need the prior knowledge of convexity or concavity of the performance function as other modified algorithms do. Numerical results of several highly nonlinear performance functions including an engineering application indicate that SLA is effective and robust.
in this paper, we introduce and study a new system of (A, eta)-accretive mapping inclusions in Banach spaces. Using the resolvent operator associated with (A, eta)-accretive mappings, we suggest a new general algorith...
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in this paper, we introduce and study a new system of (A, eta)-accretive mapping inclusions in Banach spaces. Using the resolvent operator associated with (A, eta)-accretive mappings, we suggest a new general algorithm and establish the existence and uniqueness of solutions for this system of (A, eta)-accretive mapping inclusions. Under certain conditions, we discuss the convergence and stability of iterative sequence generated by the algorithm. Our results extend, improve and unify many known results on variational inequalities and variational inclusions. (C) 2008 Published by Elsevier Ltd
In this paper, an iterative algorithm is established to solve discrete Lyapunov matrix equations. In this algorithm, a tuning parameter is introduced such that the iterative solution can be updated by using a combinat...
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In this paper, an iterative algorithm is established to solve discrete Lyapunov matrix equations. In this algorithm, a tuning parameter is introduced such that the iterative solution can be updated by using a combination of the information in the last step and the previous step. Some conditions for the convergence of the proposed algorithm are given. In addition, an approach is also developed to choose the optimal tuning parameter such that the algorithm achieves its fastest convergence rate. A numerical example is employed to illustrate the effectiveness of the proposed algorithm.
作者：
Ma, JunxiaDing, FengYang, ErfuJiangnan Univ
Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Peoples R China Univ Strathclyde
Strathclyde Space Inst Dept Design Mfg & Engn Management Space Mechatron Syst Technol Lab Glasgow G1 1XJ Lanark Scotland
This paper focuses on the iterative identification problems for a class of Hammerstein nonlinear systems. By decomposing the system into two fictitious subsystems, a decomposition-based least squares iterative algorit...
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This paper focuses on the iterative identification problems for a class of Hammerstein nonlinear systems. By decomposing the system into two fictitious subsystems, a decomposition-based least squares iterative algorithm is presented for estimating the parameter vector in each subsystem. Moreover, a data filtering-based decomposition least squares iterative algorithm is proposed. The simulation results indicate that the data filtering-based least squares iterative algorithm can generate more accurate parameter estimates than the least squares iterative algorithm.
This paper proposes an iterative algorithm to solve the inverse displacement for a hyper-redundant elephant's trunk robot (HRETR). In this algorithm, each parallel module is regarded as a geometric line segment an...
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This paper proposes an iterative algorithm to solve the inverse displacement for a hyper-redundant elephant's trunk robot (HRETR). In this algorithm, each parallel module is regarded as a geometric line segment and point model. According to the forward approximation and inverse pose adjustment principles, the iteration process can be divided into forward and backward iteration. This iterative algorithm transforms the inverse displacement problem of the HRETR into the parallel module's inverse displacement problem. Considering the mechanical joint constraints, multiple iterations are carried out to ensure that the robot satisfies the required position error. Simulation results show that the algorithm is effective in solving the inverse displacement problem of HRETR.
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