A Smoothing Newton Method for the Box Constrained Variational Inequality Problems

The box constrained variational inequality problem can be reformulated as a nonsmooth equation by using median operator.In this paper,we present a smoothing Newton method for solving the box constrained variational inequality problem based on a new smoothing approximation function.The proposed algorithm is proved to be well defined and convergent globally under weaker conditions.

GLOBAL LINEAR AND QUADRATIC ONE-STEP SMOOTHING NEWTON METHOD FOR VERTICAL LINEAR COMPLEMENTARITY PROBLEMS

A one_step smoothing Newton method is proposed for solving the vertical linear complementarity problem based on the so_called aggregation function. The proposed algorithm has the following good features: (ⅰ) It solves only one linear system of equations and does only one line search at each iteration; (ⅱ) It is well_defined for the vertical linear complementarity problem with vertical block P 0 matrix and any accumulation point of iteration sequence is its solution.Moreover, the iteration sequence is bounded for the vertical linear complementarity problem with vertical block P 0+R 0 matrix; (ⅲ) It has both global linear and local quadratic convergence without strict complementarity. Many existing smoothing Newton methods do not have the property (ⅲ).

The Smoothing Newton Method for Solving the Extended Linear Complementarity Problem

The extended linear complementarity problem(denoted by ELCP) can be reformulated as the solution of a nonsmooth system of equations. By the symmetrically perturbed CHKS smoothing function, the ELCP is approximated by a family of parameterized smooth equations. A one-step smoothing Newton method is designed for solving the ELCP. The proposed algorithm is proved to be globally convergent under suitable assumptions.

Smoothing Inexact Newton Method for Solving P_0-NCP Problems

Based on a smoothing symmetric disturbance FB-function,a smoothing inexact Newton method for solving the nonlinear complementarity problem with P0-function was proposed.It was proved that under mild conditions,the given algorithm performed global and superlinear convergence without strict complementarity.For the same linear complementarity problem(LCP),the algorithm needs similar iteration times to the literature.However,its accuracy is improved by at least 4 orders with calculation time reduced by almost 50%,and the iterative number is insensitive to the size of the LCP.Moreover,fewer iterations and shorter time are required for solving the problem by using inexact Newton methods for different initial points.

SMOOTHING NEWTON ALGORITHM FOR THE CIRCULAR CONE PROGRAMMING WITH A NONMONOTONE LINE SEARCH

In this paper, we present a nonmonotone smoothing Newton algorithm for solving the circular cone programming(CCP) problem in which a linear function is minimized or maximized over the intersection of an affine space with the circular cone. Based on the relationship between the circular cone and the second-order cone(SOC), we reformulate the CCP problem as the second-order cone problem(SOCP). By extending the nonmonotone line search for unconstrained optimization to the CCP, a nonmonotone smoothing Newton method is proposed for solving the CCP. Under suitable assumptions, the proposed algorithm is shown to be globally and locally quadratically convergent. Some preliminary numerical results indicate the effectiveness of the proposed algorithm for solving the CCP.

A Regularization Semismooth Newton Method for P_(0)-NCPs with a Non-monotone Line Search

In this paper,we propose a regularized version of the generalized NCPfunction proposed by Hu,Huang and Chen[J.Comput.Appl.Math.,230(2009),pp.69–82].Based on this regularized function,we propose a semismooth Newton method for solving nonlinear complementarity problems,where a non-monotone line search scheme is used.In particular,we show that the proposed non-monotone method is globally and locally superlinearly convergent under suitable assumptions.We test the proposed method by solving the test problems from MCPLIB.Numerical experiments indicate that this algorithm has better numerical performance in the case of p=5 andθ∈[0.25,075]than other cases.

垂直线性互补问题的一步全局线性和局部二次收敛光滑Newton法

基于凝聚函数,提出一个求解垂直线性互补问题的光滑Newton法· 该算法具有以下优点:(ⅰ)每次迭代仅需解一个线性系统和实施一次线性搜索;(ⅱ)算法对垂直分块P0矩阵的线性互补问题有定义且迭代序列的每个聚点都是它的解· 而且,对垂直分块P0+R0矩阵的线性互补问题,算法产生的迭代序列有界且其任一聚点都是它的解;(ⅲ)在无严格互补条件下证得算法即具有全局线性收敛性又具有局部二次收敛性· 许多已存在的求解此问题的光滑Newton法都不具有性质(ⅲ)

求解一类投资组合问题的半光滑Newton法

给出求解一类投资组合问题的半光滑Newton法,并对算法进行收敛性分析,数值例子表明新方法的高效率.

光滑化Newton法在网络平衡模型中的应用

建立了由制造商、分销商、顾客组成的带有竞争性的需求不确定的供应链网络平衡模型,采用收敛速度快的光滑化Newton法求解.最后通过算例说明用光滑化Newton法计算很快就能得到平衡解.

非线性互补问题的一种全局收敛的显式光滑Newton方法

本文针对P0 函数非线性互补问题 ,给出了一种显式光滑Newton方法 ,该方法将光滑参数μ进行显式迭代而不依赖于Newton方向的搜索过程 ,并在适当的假设条件下 。

求解非线性互补问题的一种修正的光滑Newton法

针对非线性互补问题,给出了一种修正的光滑Newton法,该方法不仅放宽了对函数F的要求,而且光滑因子的选择形式简单.在适当的条件下,证明了该算法具有全局收敛性.

Superlinear/Quadratic One-step Smoothing Newton Method for P_0-NCP

We propose a one–step smoothing Newton method for solving the non-linearcomplementarity problem with P 0–function (P_0–NCP) based on the smoothing symmetric perturbedFisher function (for short, denoted as the SSPF–function). The proposed algorithm has to solve onlyone linear system of equations and performs only one line search per iteration. Without requiringany strict complementarity assumption at the P_0–NCP solution, we show that the proposed algorithmconverges globally and superlinearly under mild conditions. Furthermore, the algorithm has localquadratic convergence under suitable conditions. The main feature of our global convergence resultsis that we do not assume a priori the existence of an accumulation point. Compared to the previousliteratures, our algorithm has stronger convergence results under weaker conditions.

一种基于Newton-Armijo优化的多项式光滑孪生支持向量机

针对光滑孪生支持向量机(smooth twin support vector machines,STWSVM)采用的Sigmoid光滑函数逼近精度低的问题,提出一种基于Newton-Armijo优化的多项式光滑孪生支持向量机(polynomial smooth twin support vector machines based on Newton-Armijo optimization,PSTWSVM-NA)。在PSTWSVM-NA中,引入正号函数,将孪生支持向量机的两个二次规划问题转化为两个不可微的无约束优化问题。随后,引入一族多项式光滑函数对不可微的无约束优化问题进行光滑逼近,并用收敛速度快的Newton-Armijo方法求解新模型。从理论上证明了PSTWSVM-NA模型具有任意阶光滑性,在人工数据和UCI数据集上的实验结果表明该算法具有较高的分类精度和较快的训练效率。

A smoothing inexact Newton method for P0 nonlinear complementarity problem

We first propose a new class of smoothing functions for the non- linear complementarity function which contains the well-known Chen-Harker- Kanzow-Smale smoothing function and Huang-Han-Chen smoothing function as special cases, and then present a smoothing inexact Newton algorithm for the P0 nonlinear complementarity problem. The global convergence and local superlinear convergence are established. Preliminary numerical results indicate the feasibility and efficiency of the algorithm.

SOLVING A CLASS OF INVERSE QP PROBLEMS BY A SMOOTHING NEWTON METHOD

We consider an inverse quadratic programming （IQP） problem in which the parameters in the objective function of a given quadratic programming （QP） problem are adjusted as little as possible so that a known feasible solution becomes the optimal one. This problem can be formulated as a minimization problem with a positive semidefinite cone constraint and its dual （denoted IQD（A, b）） is a semismoothly differentiable （SC^1） convex programming problem with fewer variables than the original one. In this paper a smoothing Newton method is used for getting a Karush-Kuhn-Tucker point of IQD（A, b）. The proposed method needs to solve only one linear system per iteration and achieves quadratic convergence. Numerical experiments are reported to show that the smoothing Newton method is effective for solving this class of inverse quadratic programming problems.

AN INEXACT SMOOTHING NEWTON METHOD FOR EUCLIDEAN DISTANCE MATRIX OPTIMIZATION UNDER ORDINAL CONSTRAINTS

When the coordinates of a set of points are known, the pairwise Euclidean distances among the points can be easily computed. Conversely, if the Euclidean distance matrix is given, a set of coordinates for those points can be computed through the well known classical Multi-Dimensional Scaling （MDS）. In this paper, we consider the case where some of the distances are far from being accurate （containing large noises or even missing）. In such a situation, the order of the known distances （i.e., some distances are larger than others） is valuable information that often yields far more accurate construction of the points than just using the magnitude of the known distances. The methods making use of the order information is collectively known as nonmetric MDS. A challenging computational issue among all existing nonmetric MDS methods is that there are often a large number of ordinal constraints. In this paper, we cast this problem as a matrix optimization problem with ordinal constraints. We then adapt an existing smoothing Newton method to our matrix problem. Extensive numerical results demonstrate the efficiency of the algorithm, which can potentially handle a very large number of ordinal constraints.

A smoothing Newton method for mathematical programs constrained by parameterized quasi-variational inequalities

We consider a class of mathematical programs governed by parameterized quasi-variational inequalities(QVI).The necessary optimality conditions for the optimization problem with QVI constraints are reformulated as a system of nonsmooth equations under the linear independence constraint qualification and the strict slackness condition.A set of second order sufficient conditions for the mathematical program with parameterized QVI constraints are proposed,which are demonstrated to be sufficient for the second order growth condition.The strongly BD-regularity for the nonsmooth system of equations at a solution point is demonstrated under the second order sufficient conditions.The smoothing Newton method in Qi-Sun-Zhou [2000] is employed to solve this nonsmooth system and the quadratic convergence is guaranteed by the strongly BD-regularity.Numerical experiments are reported to show that the smoothing Newton method is very effective for solving this class of optimization problems.

半无限优化的光滑化拟Newton法及其在最优潮流中的应用

提出求解半无限优化(SIP)问题的一类新算法—光滑化拟Newton法.基于非线性互补函数(non linearcomp lem entary prob lem-NCP function),转化SIP问题的KKT系统为非光滑方程组,设计光滑化拟Newton法求解该方程系统.该方法的特点是在每步迭代中只需求解一个线性方程组系统,且算法具有较好的全局与局部超线性收敛性.利用该方法求解电力系统暂态稳定约束的最优潮流(optim al power flows w ith transient stab ility constraints-OTS)问题,计算结果显示该算法的有效性.

Truncated Smoothing Newton Method for l_∞ Fitting Rotated Cones

In this paper, the rotated cone fitting problem is considered. In case the measured data are generally accurate and it is needed to fit the surface within expected error bound, it is more appropriate to use l∞ norm than 12 norm. l∞ fitting rotated cones need to minimize, under some bound constraints, the maximum function of some nonsmooth functions involving both absolute value and square root functions. Although this is a low dimensional problem, in some practical application, it is needed to fitting large amount of cones repeatedly, moreover, when large amount of measured data are to be fitted to one rotated cone, the number of components in the maximum function is large. So it is necessary to develop efficient solution methods. To solve such optimization problems efficiently, a truncated smoothing Newton method is presented. At first, combining aggregate smoothing technique to the maximum function as well as the absolute value function and a smoothing function to the square root function, a monotonic and uniform smooth approximation to the objective function is constructed. Using the smooth approximation, a smoothing Newton method can be used to solve the problem. Then, to reduce the computation cost, a truncated aggregate smoothing technique is applied to give the truncated smoothing Newton method, such that only a small subset of component functions are aggregated in each iteration point and hence the computation cost is considerably reduced.

基于自适应分块和联合优化光滑l_(0)范数的二维压缩感知算法

传统的压缩感知模型和重构方法,虽能有效减少数据量,但压缩和重构性能不佳,故该文提出一种基于自适应分块和联合优化光滑l_(0)范数(SL0)的2维压缩感知算法。压缩过程利用灰度熵和四叉树算法进行自适应分块和采样率分配,同时对压缩模型改进,使用混沌循环矩阵作为测量矩阵,提升了压缩性能。重构过程基于SL0算法,采用陡峭性更高的拟合函数,结合拟牛顿法和动态迭代的方案提高重构质量和效率。该算法峰值信噪比和结构相似性指数相比现有算法平均提升了5.44 dB和21.08%,平均计算时间仅需1.59 s,表明该算法能稳定、快速地实现图像的压缩感知和精确重构,为压缩感知和图像重构提供了新方法。