A self-adaptive method for solving general mixed variational inequalities

The general mixed variational inequality containing a nonlinear term φ is a useful and an important generalization of variational inequalities. The projection method cannot be applied to solve this problem due to the presence of the nonlinear term. To overcome this disadvantage, Noor [M.A. Noor, Pseudomonotone general mixed variational inequalities, Appl. Math. Comput. 141 (2003) 529-540] used the resolvent equations technique to suggest and analyze an iterative method for solving general mixed variational inequalities. In this paper, we present a new self-adaptive iterative method which can be viewed as a refinement and improvement of the method of Noor. Global convergence of the new method is proved under the same assumptions as Noor's method. Some preliminary computational results are given.     

Trajectory planning based on non-convex global optimization for serial manipulators

To perform specific tasks in dynamic environments, robots are required to rapidly update trajectories according to changing factors. A continuous trajectory planning methodology for serial manipulators based on non-convex global optimization is presented in this paper. First, a kinematic trajectory planning model based on non-convex optimization is constructed to balance motion rapidity and safety. Then, a model transformation method for the non-convex optimization model is presented. In this way, the accurate global solution can be obtained with an iterative solver starting from arbitrary initializations, which can greatly improve the computational accuracy and efficiency. Furthermore, an efficient initialization method for the iterative solver based on multivariable-multiple regression is presented, which further speeds up the solution process. The results show that trajectory planning efficiency is significantly enhanced by model transformation and initialization improvement for the iterative solver. Consequently, real-time continuous trajectory planning for serial manipulators with many degrees of freedom can be achieved, which lays a basis for performing dynamic tasks in complex environments.     

Application of the multigrid approach for solving 3D Navier-Stokes equations on hexahedral grids using the discontinuous Galerkin method

The Galerkin method with discontinuous basis functions is adapted for solving the Euler and Navier-Stokes equations on unstructured hexahedral grids. A hybrid multigrid algorithm involving the finite element and grid stages is used as an iterative solution method. Numerical results of calculating the sphere inviscid flow, viscous flow in a bent pipe, and turbulent flow past a wing are presented. The numerical results and the computational cost are compared with those obtained using the finite volume method.     

A technique to choose the most efficient method between secant method and some variants

A few variants of the secant method for solving nonlinear equations are analyzed and studied. In order to compute the local order of convergence of these iterative methods a development of the inverse operator of the first order divided differences of a function of several variables in two points is presented using a direct symbolic computation. The computational efficiency and the approximated computational order of convergence are introduced and computed choosing the most efficient method among the presented ones. Furthermore, we give a technique in order to estimate the computational cost of any iterative method, and this measure allows us to choose the most efficient among them.     

Modified Extragradient Method for Variational Inequalities and Verification of Solution Existence

In this paper, we propose a modified extragradient method for solving variational inequalities (VI) which has the following nice features: (i) The generated sequence possesses an expansion property with respect to the starting point; (ii) the existence of the solution to a VI problem can be verified through the behavior of the generated sequence from the fact that the iterative sequence diverges to infinity if and only if the solution set is empty. Global convergence of the method is guaranteed under mild conditions. Our preliminary computational experience is also reported.     

The sphere method and the robustness of the ellipsoid algorithm

We present a method for implementing the ellipsoid algorithm, whose basic iterative step is a linear row manipulation on the matrix of inequalities. This step is somewhat similar to a simplex iteration, and may give a clue to the relation between the two algorithms. Geometrically, the step amounts to performing affine transformations which map the ellipsoids onto a fixed sphere. The method was tried successfully on linear programs with up to 50 variables, some of which required more than 24 000 iterations. Geometrical properties of the iteration suggest that the ellipsoid algorithm is numerically robust, which is supported by our computational experience.     

Matrix-free interior point method

In this paper we present a redesign of a linear algebra kernel of an interior point method to avoid the explicit use of problem matrices. The only access to the original problem data needed are the matrix-vector multiplications with the Hessian and Jacobian matrices. Such a redesign requires the use of suitably preconditioned iterative methods and imposes restrictions on the way the preconditioner is computed. A two-step approach is used to design a preconditioner. First, the Newton equation system is regularized to guarantee better numerical properties and then it is preconditioned. The preconditioner is implicit, that is, its computation requires only matrix-vector multiplications with the original problem data. The method is therefore well-suited to problems in which matrices are not explicitly available and/or are too large to be stored in computer memory. Numerical properties of the approach are studied including the analysis of the conditioning of the regularized system and that of the preconditioned regularized system. The method has been implemented and preliminary computational results for small problems limited to 1 million of variables and 10 million of nonzero elements demonstrate the feasibility of the approach.     

Adjacency based method for generating maximal efficient faces in multiobjective linear programming

Multiobjective linear optimization problems (MOLPs) arise when several linear objective functions have to be optimized over a convex polyhedron. In this paper, we propose a new method for generating the entire efficient set for MOLPs in the outcome space. This method is based on the concept of adjacencies between efficient extreme points. It uses a local exploration approach to generate simultaneously efficient extreme points and maximal efficient faces. We therefore define an efficient face as the combination of adjacent efficient extreme points that define its border. We propose to use an iterative simplex pivoting algorithm to find adjacent efficient extreme points. Concurrently, maximal efficient faces are generated by testing relative interior points. The proposed method is constructive such that each extreme point, while searching for incident faces, can transmit some local informations to its adjacent efficient extreme points in order to complete the faces’ construction. The performance of our method is reported and the computational results based on randomly generated MOLPs are discussed.     

Fifth order implicit multipoint method for solving equations

We have derived a two-stage implicit fifth order multipoint iterative method for solving equations. Further, we have also obtained an implicit third order and a semiexplicit fourth order multipoint method. Comparison of computational results are made with other well-known methods on a number of difficult problems. The implicit multipoint methods are accurate and robust.     

Analysis of the backward-euler/langevin method for molecular dynamics

This paper develops the theory of a recently introduced computational method for molecular dynamics. The method in question uses the backward-Euler method to solve the classical Langevin equations of a molecular system. Parameters are chosen to produce a cutoff frequency ω_c, which may be set equal to kT/h to simulate quantum-mechanical effects. In the present paper, an ensemble of identical Hamiltonian systems modeled by the backward-Euler/Langevin method is considered, an integral equation for the equilibrium phase-space density is derived, and an asymptotic analysis of that integral equation in the limit Δt → 0 is performed. The result of this asymptotic analysis is a second-order partial differential equation for the equilibrium phase-space density expressed as a function of the constants of the motion. This equation is solved in two special cases: a system of coupled harmonic oscillators and a diatomic molecule with a stiff bond.     

Newton's method for linear complementarity problems

This paper presents an iterative, Newton-type method for solving a class of linear complementarity problems. This class was discovered by Mangasarian who had established that these problems can be solved as linear programs. Cottle and Pang characterized solutions of the problems in terms of least elements of certain polyhedral sets. The algorithms developed in this paper are shown to converge to the least element solutions. Some applications and computational results are also discussed.     

Leap-frogging Newton's method

Using Newton's method as an intermediate step, we introduce an iterative method that approximates numerically the solution of f (x) = 0. The method is essentially a leap-frog Newton's method. The order of convergence of the proposed method at a simple root is cubic and the computational efficiency in general is less, but close to that of Newton's method. Like Newton's method, the new method requires only function and first derivative evaluations. The method can easily be implemented on computer algebra systems where high machine precision is available.     

A numerical method for an inhomogeneous nonlinear diffusion problem

A numerical method for the solution of an inhomogeneous nonlinear diffusion problem that arises in a variety of applications is presented. The diffusion coefficient in the underlying diffusion process is concentration- as well as distance- dependent. We wish to determine the concentration of the diffusing substance in a semi-infinite domain at any time, starting with a given initial concentration. The method of solution begins by first mapping the semi-infinite physical domain to a finite computational domain. An implicit finite-difference marching procedure is then used to advance the solution in time. Numerical results are presented for several physical problems. We observe that the present numerical solutions are in good agreement with the analytical solutions obtained previously by other researchers.     

A new improved Adomian decomposition method and its application to fractional differential equations

In this paper, a new improved Adomian decomposition method is proposed, which introduces a convergence-control parameter into the standard Adomian decomposition method and establishes a new iterative formula. The examples prove that the presented method is reliable, efficient, easy to implement from a computational viewpoint and can be employed to derive successfully analytical approximate solutions of fractional differential equations.     

A very simple SQCQP method for a class of smooth convex constrained minimization problems with nice convergence results

We introduce a new and very simple algorithm for a class of smooth convex constrained minimization problems which is an iterative scheme related to sequential quadratically constrained quadratic programming methods, called sequential simple quadratic method (SSQM). The computational simplicity of SSQM, which uses first-order information, makes it suitable for large scale problems. Theoretical results under standard assumptions are given proving that the whole sequence built by the algorithm converges to a solution and becomes feasible after a finite number of iterations. When in addition the objective function is strongly convex then asymptotic linear rate of convergence is established.     

通带可调相位 FIR 数字滤波器及其设计

常见的 FIR 数字滤波器大致可分为两类:一类是线性相位滤波器;另一类是极小相位滤波器,这两种类型的滤波器,其相位响应均不可调.第一种滤波器,其群延迟为((N-1)T)/2,其中 N 为滤波器长度,T 为采样周期.以 N=60,T=0.2秒的线性相位滤波器为例,它的相位响应为     

The nullspace method for the three-dimensional Stokes problem

In this paper, we present the preconditioned nullspace method for the iterative solution of the three-dimensional Stokes problem. In the nullspace method, the original saddle point system is reduced to a positive definite problem by representing the solution with respect to a basis of discretely divergence free vectors. The exact, explicit computation of such a basis typically has non-optimal (storage and computational) complexity. There exist some algorithms that exploit the sparsity of the matrix and work well for two dimensional problems but fail for three dimensions. Here, we will exploit an implicit representation of the nullspace basis which can be computed efficiently also in a three-dimensional setting, possibly only as an approximation. We will present some numerical results to illustrate the performance of the resulting solution method. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Analysis of statically indeterminate non-uniform bar problem in post elastic domain by an iterative variational method

The present study investigates the growth of elastic–plastic front of a statically indeterminate non-uniform bar in post-elastic regime. The solutions of statically indeterminate bar problems are critical in general, because they are not amenable to a ready analytical solution. A clamped axially loaded bar problem becomes indeterminate when the load is concentric, and it result in a singularity point in the domain. In the present bar problem more such singularity points arise when the bar is in post-elastic state, at higher magnitude of concentrated load and the other points come from the yield front location. The computational domain is divided into sub-domains based on the location of singularity points. The formulation is based on von-Mises yield criterion and for linear strain hardening type material behavior. The governing equation is derived through an extension of a variational method in elasto-plastic regime and solution is obtained by using Galerkin's approximation principle. The approximate solution further needs an iterative method to locate the growth in the yield front. The solution algorithm is implemented with the help of MATLAB^® computational simulation software and validation of the formulation is carried out successfully for some reduced problems. The effect of geometry parameters like aspect ratio, slenderness ratio and the type of taperness on the post-elastic performance of the bar is investigated and the relevant results are obtained in dimensionless form. The term bar used in this paper is in generic sense and hence the formulation is applicable for all one dimensional elements, e.g., rods, pipes, truss members, etc.     

Construction of Hamiltonian cycles by recurrent neural networks in graphs of distributed computer systems

An algorithm based on a recurrent neural Wang’s network and the WTA (“Winner takes all”) principle is applied to the construction of Hamiltonian cycles in graphs of distributed computer systems (CSs). The algorithm is used for: 1) regular graphs (2D- and 3D-tori, and hypercubes) of distributed CSs and 2) 2D-tori disturbed by removing an arbitrary edge. The neural network parameters for the construction of Hamiltonian cycles and suboptimal cycles with a length close to that of Hamiltonian ones are determined. Our experiments show that the iterative method (Jacobi, Gauss-Seidel, or SOR) used for solving the system of differential equations describing a neural network strongly affects the process of cycle construction and depends on the number of torus nodes.     

Testing the topographical global initialization strategy in the framework of an unconstrained optimization method

In general, classical iterative algorithms for optimization, such as Newton-type methods, perform only local search around a given starting point. Such feature is an impediment to the direct use of these methods to global optimization problems, when good starting points are not available. To overcome this problem, in this work we equipped a Newton-type method with the topographical global initialization strategy, which was employed together with a new formula for its key parameter. The used local search algorithm is a quasi-Newton method with backtracking. In this approach, users provide initial sets, instead of starting points. Then, using points sampled in such initial sets (merely boxes in \({\mathbb {R}}^{n}\)), the topographical method selects appropriate initial guesses for global optimization tasks. Computational experiments were performed using 33 test problems available in literature. Comparisons against three specialized methods (DIRECT, MCS and GLODS) have shown that the present methodology is a powerful tool for unconstrained global optimization.