具有线性化技术的三块非凸不可分优化问题BregmanADMM收敛性分析

交替方向乘子法是求解两块可分离凸优化问题的有效方法,但是对于三块不可分的非凸优化问题的交替方向乘子法的收敛性可能无法保证.该文主要研究的是用线性化广义Bregman交替方向乘子法(L-G-BADMM)求解目标函数是三块不可分的非凸极小化问题的收敛性分析.在适当假设条件下,对算法中子问题进行求解并构建满足Kurdyka-Lojasiewicz性质的效益函数,经过理论证明可以得到该算法的收敛性.     

可数族弱Bregman相对非扩展映像的收敛性分析

在自反Banach空间中,引入可数族弱Bregman相对非扩张映像概念,构造了两种迭代算法求解可数族弱Bregman相对非扩张映像的公共不动点.在适当条件下,证明了两种迭代算法产生的序列的强收敛性.     

A-线性Bregman 迭代算法

线性Bregman迭代是Osher和Cai等人最近提出的一种在压缩感知等领域有重要作用的有效算法.本文在矩阵A非满秩情形下,研究了求解下面最优化问题的线性Bregman迭代:min u∈R~M{‖u‖_1:Au+g}给出了一个关于线性Bregman迭代收敛性定理的简化证明,设计了一类A~-线性Bregman迭代算法,并针对A~+情形证明了算法的收敛性.最后,用数值仿真实验验证了本文算法的可行性.     

基于Armijo线搜索的对角三阶拟柯西法

通过引入基于最小改变的对角修正策略,结合三阶拟牛顿方程,提出了基于Armijo线搜索的对角三阶拟柯西法.在适当的假设下,算法保证了修正矩阵的非奇异性,并证明了算法的线性收敛性.数值试验表明该算法是有效的.     

一个无正则条件下一般约束最优化问题的信赖域算法的收敛性

本文我们对[1]的算法给出一个修正并在无正则条件下对这一算法给出了收敛性分析，与[1]不同，我们不需要(SBSQ)约束条件。因此本文的结果是[1]的结果的推广和加强。     

矩阵填充的硬阈值修正算法

提出了使用硬阈值进行矩阵填充的修正算法.算法通过对迭代矩阵进行对角修正来完成矩阵填充,并给出了算法的收敛性分析.最后通过数值实验比较了修正算法与硬阈值算法填充的数值结果,显示出了新算法的优越性.     

随机Bregman ADMM及其在训练具有离散结构的支持向量机中的应用

针对具有多块可分结构的非凸优化问题提出了一类新的随机Bregman交替方向乘子法,在周期更新规则下, 证明了该算法的渐进收敛性; 在随机更新的规则下, 几乎确定的渐进收敛性得以证明。数值实验结果表明, 该算法可有效训练具有离散结构的支持向量机。     

修正Hestenes-Stiefel共轭梯度算法

本文探讨了Hestenes－Stiefel（HS）共轭梯度算法的收敛性条件．在无充分下降性条件下，证明了一种修正的HS共轭梯度算法的整体收敛性．     

一类不精确拟牛顿型算法的局部收敛性分析

为了求解Hilbert空间中算子方程或minimax问题,构造了一类无穷维空间中的不精确拟牛顿算法,并考虑了其线性收敛性和超线性收敛性,是对有限维空间中不精确拟牛顿法的推广.当迭代算子由Broyden修正给出时,在一定的假设条件下,得到了不精确Broyden方法的线性收敛性和超线性收敛性.这为使用不精确拟牛顿法结合投影法求解算子方程做好了准备.     

大型稀疏无约束最优化问题的行列修正算法

本文提出了一类适用于大型稀疏最优化问题的简单易行的行列修正算法，获得了新算法的局部超一性收敛性，大量的数值试验表明这是一个较为理想的修正算不。新算法同样可以用来求解大型对称性非线性方程组。     

Neighborhood-following algorithms for linear programming

In this paper, we present neighborhood-following algorithms for linear programming. When the neighborhood is a wide neighborhood, our algorithms are wide neighborhood primal-dual interior point algorithms. If the neighborhood degenerates into the central path, our algorithms also degenerate into path-following algorithms. We prove that our algorithms maintain the O(n~(1/2)L)-iteration complexity still, while the classical wide neighborhood primal-dual interior point algorithms have only the O(nL)-iteration complexity. We also proved that the algorithms are quadratic convergence if the optimal vertex is nondegenerate. Finally, we show some computational results of our algorithms.     

Implicit iterative algorithms of the split common fixed point problem for Bregman quasi-nonexpansive mapping in Banach spaces

In this paper, we study a modified implicit rule for finding a solution of split common fixed point problem of a Bregman quasi-nonexpansive mapping in Banach spaces. We propose a new iterative algorithm and prove the strong convergence theorem under appropriate conditions. As an application, the results are applied to solving the zero problem and the equilibrium problem.     

Convergence analysis for system of equilibrium problems and left bregman strongly relatively nonexpansive mapping

In this paper, we prove strong convergence theorems for approximation of a fixed point of a left Bregman strongly relatively nonexpansive mapping which is also a solution to a finite system of equilibrium problems in the framework of reflexive real Banach spaces. We also discuss the approximation of a common fixed point of a family of left Bregman strongly nonexpansive mappings which is also solution to a finite system of equilibrium problems in reflexive real Banach spaces. Our results complement many known recent results in the literature.     

CONVERGENCE ANALYSIS FOR SYSTEM OF EQUILIBRIUM PROBLEMS AND LEFT BREGMAN STRONGLY RELATIVELY NONEXPANSIVE MAPPING

In this paper, we prove strong convergence theorems for approximation of a fixed point of a left Bregman strongly relatively nonexpansive mapping which is also a solution to a finite system of equilibrium problems in the framework of reflexive real Banach spaces. We also discuss the approximation of a common fixed point of a family of left Bregman strongly nonexpansive mappings which is also solution to a finite system of equilibrium problems in reflexive real Banach spaces. Our results complement many known recent results in the literature.     

A proximal method with separable Bregman distances for quasiconvex minimization over the nonnegative orthant

We present an interior proximal method with Bregman distance, for solving the minimization problem with quasiconvex objective function under nonnegative constraints. The Bregman function is considered separable and zone coercive, and the zone is the interior of the positive orthant. Under the assumption that the solution set is nonempty and the objective function is continuously differentiable, we establish the well definedness of the sequence generated by our algorithm and obtain two important convergence results, and show in the main one that the sequence converges to a solution point of the problem when the regularization parameters go to zero.     

Halpern's Iteration for Bregman Relatively Nonexpansive Mappings in Banach Spaces

In this article, using Bregman functions, we first introduce new modified Mann and Halpern's iterations for finding common fixed points of an infinite family of Bregman relatively nonexpansive mappings in the framework of Banach spaces. Furthermore, we prove the strong convergence theorems for the sequences produced by the methods. Finally, we apply these results for approximating zeroes of accretive operators. Our results improve and generalize many known results in the current literature.     

Iterative Methods of Solving Stochastic Convex Feasibility Problems and Applications

The stochastic convex feasibility problem (SCFP) is the problem of finding almost common points of measurable families of closed convex subsets in reflexive and separable Banach spaces. In this paper we prove convergence criteria for two iterative algorithms devised to solve SCFPs. To do that, we first analyze the concepts of Bregman projection and Bregman function with emphasis on the properties of their local moduli of convexity. The areas of applicability of the algorithms we present include optimization problems, linear operator equations, inverse problems, etc., which can be represented as SCFPs and solved as such. Examples showing how these algorithms can be implemented are also given.     

An Infeasible Mizuno–Todd–Ye Type Algorithm for Convex Quadratic Programming with Polynomial Complexity

Infeasible interior point methods have been very popular and effective. In this paper, we propose a predictor–corrector infeasible interior point algorithm for convex quadratic programming, and we prove its convergence and analyze its complexity. The algorithm has the polynomial numerical complexity with O(nL)-iteration.     

A Class of Modified Broyden Algorithms

S1.IntroductionWeknowthatthevariablemetricalgorithms,suchastheBroydenalgorithms,areveryusefulandefficientmethodsforsolvingthenonlinearprogrammingproblem'min{f(x);xER"}-(1.1)Withexactlinearsearch,Powell(1971)provesthattherateofconvergenceofthesealgorithmsisone-stepsuperlinearfortheuniformlyconvexobjectivefunction,andifthepointsgivenbytheseaJgorithmsareconvergent,PuandYu(199o)provethattheyaregloballyconvergentforthecontinuousdifferentiablefunction.Withoutexactlinearsearchseveralresultshavebee…