Strong convergence for large bodies in micromagnetics

The convexified Landau-Lifshitz minimisation problem in micromagnetics leads to a degenerate variational problem. Therefore strong convergence of finite element approximations cannot be expected in general. This paper introduces a stabilised finite element discretisation which allows for the strong convergence of the discrete magnetisation fields with reduced convergence order for a uniaxial model problem. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

A geometric theory for preconditioned inverse iteration IV: On the fastest convergence cases

In order to compute the smallest eigenvalue together with an eigenfunction of a self-adjoint elliptic partial differential operator one can use the preconditioned inverse iteration scheme, also called the preconditioned gradient iteration. For this iterative eigensolver estimates on the poorest convergence have been published by several authors. In this paper estimates on the fastest possible convergence are derived. To this end the convergence problem is reformulated as a two-level constrained optimization problem for the Rayleigh quotient. The new convergence estimates reveal a wide range between the fastest possible and the slowest convergence.     

Quasi-stationary Stefan problem as limit case of Mullins-Sekerka problem

The existence of a local classical solution to the Mullins-Sekerka problem and the convergence to the two-phase quasi-stationary Stefan problem are proved when surface tension approaches zero. This convergence gives a proof of the existence of a local classical solution of quasi-stationary Stefan problem. The methods work in all dimensions.     

On finite convergence of proximal point algorithms for variational inequalities

In this paper, we first characterize finite convergence of an arbitrary iterative algorithm for solving the variational inequality problem (VIP), where the finite convergence means that the algorithm can find an exact solution of the problem in a finite number of iterations. By using this result, we obtain that the well-known proximal point algorithm possesses finite convergence if the solution set of VIP is weakly sharp. As an extension, we show finite convergence of the inertial proximal method for solving the general variational inequality problem under the condition of weak g-sharpness.     

Homogenization of Optimal Control Problems for Functional Differential Equations

The paper deals with the variational convergence of a sequence of optimal control problems for functional differential state equations with deviating argument. Variational limit problems are found under various conditions of convergence of the input data. It is shown that, upon sufficiently weak assumptions on convergence of the argument deviations, the limit problem can assume a form different from that of the whole sequence. In particular, it can be either an optimal control problem for an integro-differential equation or a purely variational problem. Conditions are found under which the limit problem preserves the form of the original sequence.     

A non-interior-point smoothing method for variational inequality problem

In this paper, we focus on the variational inequality problem. Based on the Fischer-Burmeister function with smoothing parameters, the variational inequality problem can be reformulated as a system of parameterized smooth equations, a non-interior-point smoothing method is presented for solving the problem. The proposed algorithm not only has no restriction on the initial point, but also has global convergence and local quadratic convergence, moreover, the local quadratic convergence is established without a strict complementarity condition. Preliminary numerical results show that the algorithm is promising.     

Convergence results for continuous-time adaptive stochastic filtering algorithms

The adaptive stochastic filtering problem for Gaussian processes is considered. The self-tuning synthesis procedure is used to derive two algorithms for this problem. Almost sure convergence for the parameter estimate and the filtering error will be established. The convergence analysis is based on an almost-supermartingale convergence lemma that allows a stochastic Lyapunov-like approach.     

多场址问题的信赖域算法

多场址问题是一类重要的不可微凸规划问题，国内外已有许多学者对其进行研究，并提出了一 算法。但如文「２」中所述，大多数算法或无收敛收保证，或在较强的条件下才保证收敛，本文提出一类解多场址问题的信赖域算法，并在极弱的条件下证明该类算法的全局收敛性。     

从常步长梯度方法的视角看不可微凸优化增广Lagrange方法的收敛性

增广Lagrange方法是求解非线性规划的一种有效方法.从一新的角度证明不等式约束非线性非光滑凸优化问题的增广Lagrange方法的收敛性.用常步长梯度法的收敛性定理证明基于增广Lagrange函数的对偶问题的常步长梯度方法的收敛性,由此得到增广Lagrange方法乘子迭代的全局收敛性.     

A note on a journal selection problem

This note explores the convergence properties of certain sequences of conditional probabilities arising in a journal selection problem, where the probabilities of interest are decreasing in either a deterministic or stochastic fashion. We prove the convergence to a nonextreme value of the probability of an eventual event for any choice of problem parameters within the open unit interval. Computational results illustrate the convergence properties.     

On split common fixed point problems

Based on the convergence theorem recently proved by the second author, we modify the iterative scheme studied by Moudafi for quasi-nonexpansive operators to obtain strong convergence to a solution of the split common fixed point problem. It is noted that Moudafi's original scheme can conclude only weak convergence. As a consequence, we obtain strong convergence theorems for split variational inequality problems for Lipschitz continuous and monotone operators, split common null point problems for maximal monotone operators, and Moudafi's split feasibility problem.     

Hilbert空间的一个约束极值问题和非协调有限元的加罚方法

在实践中成功地运用了非协调有限元并因此而使它受到人们的注意和研究。使用非协调元的一个方法是加罚方法。这是Babuska和Zlamal首先在[1]中提出的。冯康在[4]中证明:一般的,当惩罚项满足某些条件时,加罚方法总是收敛的。 非协调元(以及杂交元和某些其他数值解法)的理论研究,可以归结为Hilbert空间的一个约束极值问题。本文首先对这一抽象问题进行了分析,证明了加罚方法的收敛性     

Weak and Strong Convergence Theorems for New Demimetric Mappings and the Split Common Fixed Point Problem in Banach Spaces

In this paper, we consider the split common fixed point problem for new demimetric mappings in Banach spaces. Using the idea of Mann’s iteration, we prove a weak convergence theorem for finding a solution of the split common fixed point problem in Banach spaces. Furthermore, using the idea of Halpern’s iteration, we obtain a strong convergence theorem for finding a solution of the problem in Banach spaces. Using these results, we obtain well-known and new weak and strong convergence theorems in Hilbert spaces and Banach spaces.     

Convergence and optimality of the adaptive Morley element method

This paper is devoted to the convergence and optimality analysis of the adaptive Morley element method for the fourth order elliptic problem. A new technique is developed to establish a quasi-orthogonality which is crucial for the convergence analysis of the adaptive nonconforming method. By introducing a new parameter-dependent error estimator and further establishing a discrete reliability property, sharp convergence and optimality estimates are then fully proved for the fourth order elliptic problem.     

Weak convergence theorem for zero points of inverse strongly monotone mapping and fixed points of nonexpansive mapping in Hilbert space

We know that variational inequality problem is very important in the nonlinear analysis. For a variational inequality problem defined over a nonempty fixed point set of a nonexpansive mapping in Hilbert space, the strong convergence theorem has been proposed by I. Yamada. The algorithm in this theorem is named the hybrid steepest descent method. Based on this method, we propose a new weak convergence theorem for zero points of inverse strongly monotone mapping and fixed points of nonexpansive mapping in Hilbert space. Using this result, we obtain some new weak convergence theorems which are useful in nonlinear analysis and optimization problem.     

The split common fixed point problem for generalized demimetric mappings in two Banach spaces

ABSTRACT

In this paper, we consider the split common fixed point problem for new demimetric mappings in two Banach spaces. Using the hybrid method, we prove a strong convergence theorem for finding a solution of the split common fixed point problem in two Banach spaces. Furthermore, using the shrinking projection method, we obtain another strong convergence theorem for finding a solution of the problem in two Banach spaces. Using these results, we obtain well-known and new strong convergence theorems in Hilbert spaces and Banach spaces.     

Convergence for vector optimization problems with variable ordering structure

In this paper, we first discuss the Painlevé–Kuratowski set convergence of (weak) minimal point set for a convex set, when the set and the ordering cone are both perturbed. Next, we consider a convex vector optimization problem, and take into account perturbations with respect to the feasible set, the objective function and the ordering cone. For this problem, by assuming that the data of the approximate problems converge to the data of the original problem in the sense of Painlevé–Kuratowski convergence and continuous convergence, we establish the Painlevé–Kuratowski set convergence of (weak) minimal point and (weak) efficient point sets of the approximate problems to the corresponding ones of original problem. We also compare our main theorems with existing results related to the same topic.     

On the Convergence of Certain Numerical Characteristics of Variational Dirichlet Problems in Variable Domains

We prove two theorems that enable one to reduce the problem of convergence of general characteristics of variational Dirichlet problems in variable domains to the problem of convergence of simpler characteristics of these problems. We describe the case where the convergence of simpler characteristics takes place.     

Penalty method for solving a class of stochastic differential variational inequalities with an application

The aim of this paper is to study the penalty method for solving a class of stochastic differential variational inequalities (SDVIs). The penalty problem for solving SDVIs is first constructed and the convergence of the sequences generated by the penalty problem is proved under some mild conditions. As an application, the convergence of the sequences generated by the penalty problem is obtained for solving a stochastic migration equilibrium problem with movement cost.     

A linearly convergent algorithm for sparse signal reconstruction

For the sparse signal reconstruction problem in compressive sensing, we propose a projection-type algorithm without any backtracking line search based on a new formulation of the problem. Under suitable conditions, global convergence and its linear convergence of the designed algorithm are established. The efficiency of the algorithm is illustrated through some numerical experiments on some sparse signal reconstruction problem.