P0-函数箱约束变分不等式的正则半光滑牛顿法

1引言设X C R~n,F：R~n→R~n,变分不等式Ⅵ(X,F)是指：求x∈X,使F(x)~T(y-x)≥0,(?)_y∈X．(1)记i∈N={1,2,…,n},当X=[a,b]：={x∈(?)~n|a_i≤x_i≤b_i,i∈N}时,称Ⅵ(X,F)为箱约束变分不等式(也有些文献称为混合互补问题),记为Ⅵ(a,b,F)．若a_i=0,b_i= ∞,i∈N,即X=(?)_ ~n：={x∈(?)~n|x≥0}时,Ⅵ(a,b,F)化为非线性互补问题NCP(F)：求x∈(?)_ ~n,使x≥0,F(x)≥0,x~TF(x)=0．(2)     

线性规划的最钝角CRISS-CROSS算法

1 引言 考虑如下标准线性规划问题 minimize c~Tx (1) subject to Ax=b, x≥0 其中A∈R~(m×n) (m相似文献   

直线与椭圆位置关系的一个性质定理及其应用

定理　若直线l:Ax +By +C =0 (A2 +B2 ≠ 0 )与椭圆C :(x -x0 ) 2a2 + ( y - y0 ) 2b2 =1有公共点 ,则有(Aa) 2 + (Bb) 2 ≥ (Ax0 +By0 +C) 2 .证　由(x -x0 ) 2a2 + ( y - y0 ) 2b2 =1 ,可令x =x0 +acosθ,y =y0 +bsinθ ,代入Ax +By +C =0 (A2 +B2 ≠ 0 ) ,得A(x0 +acosθ) +B( y0 +bsinθ) +C =0 .整理得Aacosθ +Bbsinθ =- (Ax0 +By0 +C) .即 (Aa) 2 + (Bb) 2 sin(θ + φ) =- (Ax0 +By0 +C) (其中 φ为辅助角 ) .又 |sin(θ+ φ) |≤ 1 ,∴| - (Ax0 +By0 +C) |(Aa) 2 + (Bb) 2 ≤ 1 .即 (Aa) 2 + (Bb) 2 ≥ (Ax0 +By0…     

The schur convexity of gini mean values in the sense of harmonic mean

We prove that the Gini mean values S(a,b; x,y) are Schur harmonic convex with respect to (x,y)∈(0,∞)×(0,∞) if and only if (a, b) ∈{(a, b):a≥0,a ≥ b,a+b+1≥0}∪{(a,b):b≥0,b≥a,a+b+1≥0} and Schur harmonic concave with respect to (x,y) ∈ (0,∞)×(0,∞) if and only if (a,b)∈{(a,b):a≤0,b≤0,a|b|1≤0}.     

求解特征值互补问题的ABS算法

正1引言对给定的矩阵A∈R~(n×n)和正定阵B∈R~(n×n),特征值互补问题(EiCP)~([1-3])是指:求实数λ和向量x∈R~n\{0}使得{y=(A-λB)x y≥0,x≥0 y~Tx=0 (1)它源于工程和物理问题,如对力学接触问题和结构力学系统的稳定性的研究[3-6].EiCP也可表示为如下形式的锥约束特征值问题[7,8]:对给定的矩阵A∈R~(n×n)和正定阵B∈R~(n×n),求实数λ和向量量x∈R~n\{0}使得     

非线性规划的一个可行方向方法

<正> 本文的目的是给出非线性规划问题(P) min(?) f(x),R={x|Ax=b,x≥0}的一个具收敛性的算法.其中,f(x)∈C′,A 是 m×n 阶矩阵(m相似文献   

解一类线性互补问题的区间方法

1引言线性互补问题简记为LCP(M,q)是指对给定的n×n阶实方阵M和N维实向量q,求满足下列条件的实向量x：x≥0,Mx q≥0,(1．1) x~T(Mx q)=0．它在工程物理、管理学、经济学、约束最优化等领域有着广泛的应用背景．备受人们关注     

用点到直线距离公式解决与函数相关的问题

点P(x,y)到直线Ax By C=0距离为d=|Ax By C|/A~2 B~2,当P(x,y)在函数y=f(x)上时,该公式变为d=|Ax Bf(x) C|/A~2 B~2,本文通过引进函数y=f(x),借助该公式解决一些与函数相关的问题.1.求函数单调性例1求f(x)=|x 2-1-x2|的单调区间及单调性.分析把函数f(x)作为点线间距离,借助图象,看x变大时,该距离如何变?图1例1图解函数的定义域是-1≤x≤1,令y=1-x2,即x2 y2=1,y≥0.如图1,所以f(x)=|x 2-y|=|x 2-y|2×2,几何意义:半圆上动点M(x,y)到定直线l:x-y 2=0的距离的2倍.由图1知使OB⊥l时,B到l的距离最小,显然OB:y=-x,由x2 y2=1,(y≥0),y=-x,…     

解析几何中的四类对称变换

[复习说明 ]由于平面解析几何中所研究的许多图形是对称图形 ,于是相关的对称变换问题经常在全国高考试卷与各地模拟试卷中出现 ,它是高考复习的一个热点专题 .本专题复习的重点是两点关于直线成轴对称问题 ;难点是两曲 (直 )线关于直线成轴对称问题 .[内容提要 ]1 .点 P(x,y)关于点 M(a,b)成中心对称的点是 P′(2 a - x,2 b - y) .2 .两点 P(x1,y1)、Q(x2 ,y2 )关于直线 Ax+By +C=0 (AB≠ 0 )成轴对称的充要条件是　 A .x1+x22 +B .y1+y22 +C =0 ,且 (- AB) .y1- y2x1- x2=- 1 .特例　点 P(x,y)依次关于直线 x =a,y =b,y =x,y =- x…     

非线性规划中一个梯度投影法的收敛性质

我们考虑非线性规划问题(P)■f(x),其中R={x|Ax=a,Bx≤b},A是p×n矩阵,其秩为p,B是q×n矩阵,x∈E~n,a∈E~p,b∈E~q,f(x)∈C~1.我们以R~*表示(P)的最优解集合,并假定R非空.最近,M.S.Bazaraa与J.J.Goode     

对称双正型线性互补问题的多重网格迭代解收敛性理论

多重网格法是七十年代产生并获得迅速发展的快速送代法.八十年代初,此方法开始应用于变分不等式的求解,其中包括一类互补问题,近十年来大量的数值实验证实,算法是成功的,而算法的收敛性理论也正在逐步建立,当A正定对称时的多重网格收敛性可见[3]和[7];[4]讨论了A半正定时的情况·本文考虑A为更广的一类矩阵:对称双正阵(见定义1.1),建立互补问题:     

对称线性互补问题的乘性Schwarz算法

本文提出了求解对称性互补问题的乘性Schwarz算法,其中子问题用投影迭代方法求解.利用投影迭代算子的性质及投影迭代的收敛性,证明了算法产生的迭代点列的聚点为原互补问题的解,并在一定条件下,证明算法产生的迭代点列的聚点存在.     

On the convergence of iterative methods for symmetric linear complementarity problems

We prove convergence of the whole sequence generated by any of a large class of iterative algorithms for the symmetric linear complementarity problem (LCP), under the only hypothesis that a quadratic form associated with the LCP is bounded below on the nonnegative orthant. This hypothesis holds when the matrix is strictly copositive, and also when the matrix is copositive plus and the LCP is feasible. The proof is based upon the linear convergence rate of the sequence of functional values of the quadratic form. As a by-product, we obtain a decomposition result for copositive plus matrices. Finally, we prove that the distance from the generated sequence to the solution set (and the sequence itself, if its limit is a locally unique solution) have a linear rate of R-convergence.Research for this work was partially supported by CNPq grant No. 301280/86.     

Solution of symmetric linear complementarity problems by iterative methods

A unified treatment is given for iterative algorithms for the solution of the symmetric linear complementarity problem: $$Mx + q \geqslant 0, x \geqslant 0, x^T (Mx + q) = 0$$ , whereM is a givenn×n symmetric real matrix andq is a givenn×1 vector. A general algorithm is proposed in which relaxation may be performed both before and after projection on the nonnegative orthant. The algorithm includes, as special cases, extensions of the Jacobi, Gauss-Seidel, and nonsymmetric and symmetric successive over-relaxation methods for solving the symmetric linear complementarity problem. It is shown first that any accumulation point of the iterates generated by the general algorithm solves the linear complementarity problem. It is then shown that a class of matrices, for which the existence of an accumulation point that solves the linear complementarity problem is guaranteed, includes symmetric copositive plus matrices which satisfy a qualification of the type: $$Mx + q > 0 for some x in R^n $$ . Also included are symmetric positive-semidefinite matrices satisfying this qualification, symmetric, strictly copositive matrices, and symmetric positive matrices. Furthermore, whenM is symmetric, copositive plus, and has nonzero principal subdeterminants, it is shown that the entire sequence of iterates converges to a solution of the linear complementarity problem.     

无约束优化问题的一个下降方法

本文研究了无约束优化问题.利用当前和前面迭代点的信息以及曲线搜索技巧产生新的迭代点,得到了一个新的求解无约束优化问题的下降方法.在较弱条件下证明了算法具有全局收敛性.当目标函数为一致凸函数时,证明了算法具有线性收敛速率.初步的数值试验表明算法是有效的.     

An algorithm based on a sequence of linear complementarity problems applied to a walrasian equilibrium model: An example

The Walrasian equilibrium problem is cast as a complementarity problem, and its solution is computed by solving a sequence of linear complementarity problems (SLCP). Earlier numerical experiments have demonstrated the computational efficiency of this approach. So far, however, there exist few relevant theoretical results that characterize the performance of this algorithm. In the context of a simple example of a Walrasian equilibrium model, we study the iterates of the SLCP algorithm. We show that a particular LCP of this process may have no, one or more complementary solutions. Other LCPs may have both homogeneous and complementary solutions. These features complicate the proof of convergence for the general case. For this particular example, however, we are able to show that Lemke's algorithm computes a solution to an LCP if one exists,and that the iterative process converges globally.     

非线性不等式约束最优化一个超线性与二次收敛的强次可行方法

本文讨论非线性不等式约束最优化问题，借助于序列线性方程组技术和强次可行方法思想，建立了问题的一个初始点任意的快速收敛新算法．在每次迭代中，算法只需解一个结构简单的线性方程组．算法的初始迭代点不仅可以是任意的，而且不使用罚函数和罚参数，在迭代过程中，迭代点列的可行性单调不减．在相对弱的假设下，算法具有较好的收敛性和收敛速度，即具有整体与强收敛性，超线性与二次收敛性．文中最后给出一些数值试验结果．     

Hybrid methods with regularization for minimization problems and asymptotically strict pseudocontractive mappings in the intermediate sense

In this paper we introduce an iterative algorithm for finding a common element of the fixed point set of an asymptotically strict pseudocontractive mapping S in the intermediate sense and the solution set of the minimization problem (MP) for a convex and continuously Frechet differentiable functional in Hilbert space. The iterative algorithm is based on several well-known methods including the extragradient method, CQ method, Mann-type iterative method and hybrid gradient projection algorithm with regularization. We obtain a strong convergence theorem for three sequences generated by our iterative algorithm. In addition, we also prove a new weak convergence theorem by a modified extragradient method with regularization for the MP and the mapping S.     

一类具约束选址模型的组合算法

针对一般具闭凸集约束的单址选址模型,提出具全局收敛性的组合算法．算法在迭代中先采用信赖域技巧,当出现“内循环”时,则改用不做线搜索的梯度法．该算法既具有信赖域算法的优越性,又避免了出现“内循环”时速成的隐迭代．同时,该算法通常不需进行线搜索,较之其它组合算法更加简捷实用．     

A relaxed self-adaptive CQ algorithm for the multiple-sets split feasibility problem

The multiple-sets split feasibility problem (MSFP) is to find a point belongs to the intersection of a family of closed convex sets in one space, such that its image under a linear transformation belongs to the intersection of another family of closed convex sets in the image space. Many iterative methods can be employed to solve the MSFP. Jinling Zhao et al. proposed a modification for the CQ algorithm and a relaxation scheme for this modification to solve the MSFP. The strong convergence of these algorithms are guaranteed in finite-dimensional Hilbert spaces. Recently López et al. proposed a relaxed CQ algorithm for solving split feasibility problem, this algorithm can be implemented easily since it computes projections onto half-spaces and has no need to know a priori the norm of the bounded linear operator. However, this algorithm has only weak convergence in the setting of infinite-dimensional Hilbert spaces. In this paper, we introduce a new relaxed self-adaptive CQ algorithm for solving the MSFP where closed convex sets are level sets of some convex functions such that the strong convergence is guaranteed in the framework of infinite-dimensional Hilbert spaces. Our result extends and improves the corresponding results.