A multiplicative multisplitting method for solving the linear complementarity problem

The convergence of the multiplicative multisplitting-type method for solving the linear complementarity problem with an H-matrix is discussed using classical and new results from the theory of splitting. This directly results in a sufficient condition for guaranteeing the convergence of the multiplicative multisplitting method. Moreover, the multiplicative multisplitting method is applied to the H-compatible splitting and the multiplicative Schwarz method, separately. Finally, we establish the monotone convergence of the multiplicative multisplitting method under appropriate conditions.     

A non-monotone inexact regularized smoothing Newton method for solving nonlinear complementarity problems

In the paper [S.P. Rui and C.X. Xu, A smoothing inexact Newton method for nonlinear complementarity problems, J. Comput. Appl. Math. 233 (2010), pp. 2332–2338], the authors proposed an inexact smoothing Newton method for nonlinear complementarity problems (NCP) with the assumption that F is a uniform P function. In this paper, we present a non-monotone inexact regularized smoothing Newton method for solving the NCP which is based on Fischer–Burmeister smoothing function. We show that the proposed algorithm is globally convergent and has a locally superlinear convergence rate under the weaker condition that F is a P ₀ function and the solution of NCP is non-empty and bounded. Numerical results are also reported for the test problems, which show the effectiveness of the proposed algorithm.     

Variational iteration method for singular perturbation initial value problems

In this paper, the variational iteration method (VIM) is applied to solve singular perturbation initial value problems (SPIVPs). The obtained sequence of iterates is based on the use of Lagrange multipliers. Some convergence results of VIM for solving SPIVPs are given. Moreover, the illustrative examples show the efficiency of the method.     

Iterative algorithms for the linear complementarity problem

Direct complementary pivot algorithms for the linear complementarity problem with P-matrices are known to have exponential computational complexity. The analog of Gauss-Seidel and SOR iteration for linear complementarity problems with P-matrices has not been extensively developed. This paper extends some work of van Bokhoven to a class of nonsymmetric P-matrices, and develops and compares several new iterative algorithms for the linear complementarity problem. Numerical results for several hundred test problems are presented. Such indirect iterative algorithms may prove useful for large sparse complementarity problems.     

The variational iteration method for solving linear and nonlinear Volterra integral and integro-differential equations

The variational iteration method is used for solving the linear and nonlinear Volterra integral and integro-differential equations. The method is reliable in handling Volterra equations of the first kind and second kind in a direct manner without any need for restrictive assumptions. The method significantly reduces the size of calculations.     

A semidefinite method for tensor complementarity problems

In this paper, we study how to compute all real solutions of the tensor complimentary problem, if there are finite many ones. We formulate the problem as a sequence of polynomial optimization problems. The solutions can be computed sequentially. Each of them can be obtained by solving Lasserre's hierarchy of semidefinite relaxations. A semidefinite algorithm is proposed and its convergence properties are discussed. Some numerical experiments are also presented.     

Rank-two residue iteration method for nonnegative matrix factorization

Rank-one residue iteration (RRI) is a recently developed block coordinate method for nonnegative matrix factorization (NMF). Numerical results show that the decomposed matrices generated by RRI method may have several columns, which are zero vectors. In this paper, by studying two special kinds of quadratic programming, we develop two block coordinate methods for NMF, rank-two residue iteration (RTRI) method and rank-two modified residue iteration (RTMRI) method. In the two algorithms, the exact solution of the subproblem can be obtained directly. We also provide that the consequence generated by our proposed algorithms can converge to a stationary point. Numerical results show that the RTRI method and the RTMRI method can yield better solutions, especially RTMRI method can remedy the limitation of the RRI method.     

Asynchronous multisplitting relaxation methods for linear complementarity problems

To solve the linear complementarity problems efficiently on the high-speed multiprocessor systems, we set up a class of asynchronous parallel matrix multisplitting accelerated over-relaxation (AOR) method by technical combination of the matrix multisplitting and the accelerated overrelaxation techniques. The convergence theory of this new method is thoroughly established under the condition that the system matrix of the linear complementarity problem is an H-matrix with positive diagonal elements. At last, we also make multi-parameter extension for this new asynchronous multisplitting AOR method, and investigate the convergence property of the resulted asynchronous multisplitting unsymmetric AOR method. Thereby, an extensive sequence of asynchronous parallel relaxed iteration methods in the sense of multisplitting is presented for solving the large scale linear complementarity problems in the asynchronous parallel computing environments. This not only affords various choices, but also presents systematic convergence theories about the asynchronous parallel relaxation methods for solving the linear complementarity problems.     

Efficient preconditioned NHSS iteration methods for solving complex symmetric linear systems

Based on the new HSS (NHSS) iteration method introduced by Pour and Goughery (2015), we propose a preconditioned variant of NHSS (P*NHSS) and an efficient parameterized P*NHSS (PPNHSS) iteration methods for solving a class of complex symmetric linear systems. The convergence properties of the P*NHSS and the PPNHSS iteration methods show that the iterative sequences are convergent to the unique solution of the linear system for any initial guess when the parameters are properly chosen. Moreover, we discuss the quasi-optimal parameters which minimize the upper bounds for the spectral radius of the iteration matrices. Numerical results show that the PPNHSS iteration method is superior to several iteration methods whether the experimental optimal parameters are used or not.     

A predictor-corrector smoothing Newton method for solving the mixed complementarity problem with a P 0-function

The mixed complementarity problem (denoted by MCP(F)) can be reformulated as the solution of a nonsmooth system of equations. In the paper, based on a perturbed mid function, we contract a new smoothing function. The existence and continuity of a smooth path for solving the mixed complementarity problem with a P ₀ function are discussed. Then we presented a predictor-corrector smoothing Newton algorithm to solve the MCP with a P ₀-function. The global convergence of the proposed algorithm is verified under mild conditions. And by using the smooth and semismooth technique, the local superlinear convergence of the method is proved under some suitable assumptions.     

A Newton-like method for solving rank constrained linear matrix inequalities

This paper presents a Newton-like algorithm for solving systems of rank constrained linear matrix inequalities. Though local quadratic convergence of the algorithm is not a priori guaranteed or observed in all cases, numerical experiments, including application to an output feedback stabilization problem, show the effectiveness of the algorithm.     

Improved convergence theorems of modulus-based matrix splitting iteration method for nonlinear complementarity problems of <Emphasis Type="Italic">H</Emphasis>-matrices

In this paper, the convergence conditions of the modulus-based matrix splitting iteration method for nonlinear complementarity problem of H-matrices are weakened. The convergence domain given by the proposed theorems is larger than the existing ones. Numerical examples show the advantages of the new theorems.     

An iterative algorithm based on the piecewise linear system for solving bilateral obstacle problems

In this paper, we derive the piecewise linear system (PLS) associated with the bilateral obstacle problem and illustrate the equivalence between the linear system and finite-dimensional complementary problem. The existence and the uniqueness of the solution to the PLS are also demonstrated. Based on the PLS, a Picard iterative algorithm is proposed. The convergence analysis is given and examples are presented to verify the effectiveness of the method.     

Transfer matrix method for linear multibody system

A new method for linear hybrid multibody system dynamics is proposed in this paper. This method, named as transfer matrix method of linear multibody system (MSTMM), expands the advantages of the traditional transfer matrix method (TMM). The concepts of augmented eigenvector and equation of motion of linear hybrid multibody system are presented at first to find the orthogonality and to analyze the responses of the hybrid multibody system using modal method. If using this method, the global dynamics equation is not needed in the study of linear hybrid multibody system dynamics. The MSTMM has a small size of matrix and higher computational speed, and can be applied to linear multi-rigid-body system dynamics, linear multi-flexible-body system dynamics and linear hybrid multibody system dynamics. This method is simple, straightforward, practical, and provides a powerful tool for the study on linear hybrid multibody system dynamics. This method can be used in the following: (1) Solve the eigenvalue problem of linear hybrid multibody systems. (2) Obtain the orthogonality of eigenvectors of linear hybrid multibody systems. (3) Realize the accurate analysis of the dynamics response of linear hybrid multibody systems. (4) Find the connected parameters between bodies used in the computation of linear hybrid multibody systems. A practical engineering system is taken as an example of linear multi-rigid-flexible-body system, the dynamics model, the transfer equations and transfer matrices of various bodies and hinges; the overall transfer equation and overall transfer matrix of the system are developed. Numerical example shows that the results of the vibration characteristics and the response of the hybrid multibody system received by MSTMM and by experiment have good agreements. These validate the proposed method.     

An extrapolated splitting method for solving semi-discretized parabolic differential equations

An extrapolated Peaceman–Rachford–Strang splitting method is designed and examined in application to semi-discretized parabolic partial differential equations. A multi-product expansion method is implemented to improve the order of accuracy beyond second order in time. The numerical analysis of the high-order splitting method is further validated and illustrated through numerical experiments of linear and nonlinear partial differential equations.     

On parameterized matrix splitting preconditioner for the saddle point problems

In this paper, we present a parameterized matrix splitting (PMS) preconditioner for the large sparse saddle point problems. The preconditioner is based on a parameterized splitting of the saddle point matrix, resulting in a fixed-point iteration. The convergence theorem of the new iteration method for solving large sparse saddle point problems is proposed by giving the restrictions imposed on the parameter. Based on the idea of the parameterized splitting, we further propose a modified PMS preconditioner. Some useful properties of the preconditioned matrix are established. Numerical implementations show that the resulting preconditioner leads to fast convergence when it is used to precondition Krylov subspace iteration methods such as generalized minimal residual method.     

A population-based algorithm for solving linear assignment problems with two objectives

The paper presents a population-based algorithm for computing approximations of the efficient solution set for the linear assignment problem with two objectives. This is a multiobjective metaheuristic based on the intensive use of three operators – a local search, a crossover and a path-relinking – performed on a population composed only of elite solutions. The initial population is a set of feasible solutions, where each solution is one optimal assignment for an appropriate weighted sum of two objectives. Genetic information is derived from the elite solutions, providing a useful genetic heritage to be exploited by crossover operators. An upper bound set, defined in the objective space, provides one acceptable limit for performing a local search. Results reported using referenced data sets have shown that the heuristic is able to quickly find a very good approximation of the efficient frontier, even in situation of heterogeneity of objective functions. In addition, this heuristic has two main advantages. It is based on simple easy-to-implement principles, and it does not need a parameter tuning phase.     

A relaxed block-triangular splitting preconditioner for generalized saddle-point problems

For the generalized saddle-point problems, based on a new block-triangular splitting of the saddle-point matrix, we introduce a relaxed block-triangular splitting preconditioner to accelerate the convergence rate of the Krylov subspace methods. This new preconditioner is easily implemented since it has simple block structure. The spectral property of the preconditioned matrix is analysed. Moreover, the degree of the minimal polynomial of the preconditioned matrix is also discussed. Numerical experiments are reported to show the preconditioning effect of the new preconditioner.