求解不等式约束最优化问题的MBF—ODE方法

１引言考虑用基于修正内罚函数的常微分方程（ＭＢＦ－ＯＤＥ）方法求解下列不等式约束极小化问题：其中ｆi∈ｃ２：Ｒ,ｉ=0,１,…,ｍ．求解无约束极小化问题的ＯＤＥ的一般形式是其中,φ（ｘ）∈Ｃ１：ΩＲｎ→Ｒ;ｓ（ｘ）∈Ｃ１：ΩＲｎ→Ｒｎ且满足φ（ｘ）＞０,ｓＴ（ｘ）ｆ（ｘ）＜０,ｆ（ｘ）∈Ｃ１：Ｒｎ→Ｒ为目标函数．为便于用ＯＤＥ方法求解（１．ｌ）,可藉助于罚函数将（１．ｌ）变换为无约束极小化问题（见［７］．但由于经典罚函数（ＣＢＦ）在计算上有较大的困难,我们采用修正内罚函数（ＭＢＦ）．其基本思想是用…     

改进HS共轭梯度算法及其全局收敛性

１．引 言 １９５２年 Ｍ．Ｈｅｓｔｅｎｅｓ和Ｅ．Ｓｔｉｅｆｅｌ提出了求解正定线性方程组的共轭梯度法［１］．１９６４年Ｒ．Ｆｌｅｔｃｈｅｒ和Ｃ．Ｒｅｅｖｅｓ将该方法推广到求解下列无约束优化问题： ｍｉｎｆ（ｘ）,ｘ∈Ｒｎ,（１）其中ｆ：Ｒｎ→Ｒ１为连续可微函数,记ｇｋ＝ ｆ（ｘｋ）,ｘｋ∈ Ｒｎ． 若点列｛ｘｋ｝由如下算法产生：其中 βｋ＝［ｇＴｋ（ｇｋ－ｇｋ－１）］／［ｄＴｋ－１（ｇｋ－ｇｋ－１）］．（Ｈｅｓｔｅｎｅｓ－Ｓｔｉｅｆｅｌ）　　（４）则称该算法为 Ｈｅｓｔｅｎｅｓ—Ｓｔｉｅｆｅｌ共轭梯度算…     

由FR共轭梯度法控制的两类优化算法的全局收敛性

１引言 考虑无约束优化问题其中ｆ：Ｒｎ→Ｒ是一阶可微函数．求解（１）的非线性共轭梯度法具有如下形式：其中ｇｋ＝　ｆ（ｘｋ）,ａｋ是通过某种线搜索获得的步长,纯量βｋ的选取使得方法（２）—（３）在ｆ（ｘ）是严格凸二次函数且采用精确线搜索时化为线性共轭梯度法［１］．比较常见的βｋ的取法有Ｆｌｅｔｃｈｅｒ－Ｒｅｅｖｅｓ（ＦＲ）公式［２］和Ｐｏｌａｋ－Ｒｉｂｉｅｒｅ－Ｐｏｌｙａｋ（ＰＲＰ）公式［３－４］等．它们分别为其中　　　取欧几里得范数．对于一般非线性函数,ＦＲ方法具有较好的理论收敛性［５－６］,而…     

弱(L_1,L_2)-BLD映射的正则性

本文首先将文［１］中的ＢＬＤ映射推广为弱（Ｌ１，Ｌ２）－ＢＬＤ映射，并证明了如下正则性结果：存在两个可积指数 Ｐ１＝Ｐ１（ｎ，Ｌ１，Ｌ２）＜ｎ＜ｑ１＝ｑ１（ｎ，Ｌ１，Ｌ２），使得对任意弱（Ｌ１，Ｌ２）－ＢＬＤ映射ｆ∈（Ω，Ｒｎ），都有ｆ∈（Ω，Ｒｎ），即ｆ为（Ｌ１，Ｌ２）－ＢＬＤ映射．     

粗糙核分数次振荡积分算子的加权有界性

本文给出了一类带粗糙核的分数次振荡积分算子Ｔμ，Ｔμｆ（ｘ）＝∫ＲｎｅｉＰ（ｘ，ｙ）Ω（ｘ－ｙ）｜ｘ－ｙ｜ｎ－μｈ（｜ｘ－ｙ｜）ｆ（ｙ）ｄｙ的加权Ｌｐ（Ｒｎ）有界性．这里Ｐ（ｘ，ｙ）是Ｒｎ×Ｒｎ上非平凡的实多项式，Ω∈Ｌｑ（Ｓｎ－１）为零阶齐次函数，且ｈ（ｒ）∈ＢＶ（Ｒ＋）．作为推论，证明了Ｔμ和ＢＭＯ函数形成的高阶交换子Ｔμ，ｂ，Ｔμ，ｂｆ（ｘ）＝∫ＲｎｅｉＰ（ｘ，ｙ）Ω（ｘ－ｙ）｜ｘ－ｙ｜ｎ－μｈ（｜ｘ－ｙ｜）［ｂ（ｘ）－ｂ（ｙ）］ｍｆ（ｙ）ｄｙ也是加权Ｌｐ（Ｒｎ）有界的，其中ｂ（ｘ）∈ＢＭＯ（Ｒｎ），ｍ∈Ｚ＋     

解非线性方程组的极大熵方法

１引言考虑非线性方程组．其中Ｆ（ｘ）＝（ｆ１（ｘ）ｆ２（ｘ）,ｆ２（ｘ）,…．ｆｎ（ｘ））Ｔ．ｆｉ：Ｒｎ（ｉ＝１,…,ｎ）是连续可微实值函数．求解非线性方程组的方法多种多样,例如．以Ｎｅｗｔｏｎ法为代表的迭代法及其一些变形．以及将问题（１．１）转换为ｆ（Ｆ（ｘ））的极小化问题,等等．Ｎｅｗｔｏｎ法在理论上有许多很好的结果,但在实际计算过程中,由于例如方法对初始点的严格要求以及计算Ｆ＇（ｘ）或其相应的近似估计的困难,使方法的使用受到一定的限制．用无约束优化方法求解（１．１）时,通常将其化成一个非线…     

双特征的Beltrami方程和拟正则映射

设Ω为Ｒｎ上的一个区域，ｎ２，对于具有双特征矩阵Ｇ（ｘ），Ｈ（ｘ）∈Ｃｋ，α（Ω，Ｒｎ），ｋ１，０＜α＜１的Ｂｅｌｔｒａｍｉ方程（１．４），建立了在Ｓｏｂｏｌｅｖ空间Ｗ１，ｎｌｏｃ（Ω，Ｒｎ）上广义解的正则性：ｆ（ｘ）∈Ｃｋ＋１，δｌｏｃ（Ω），对某一δ：０＜δ＜１．     

带粗糙核的多线性振荡奇异积分

本文考虑多线性算子ＴＡｆ（ｘ）＝∫ＲｎｅｉＰ（ｘ，ｙ）Ω（ｘ－ｙ）｜ｘ－ｙ｜ｎ＋ｍＲｍ＋１（Ａ；ｘ，ｙ）ｆ（ｙ）ｄｙ，ｎ２，其中Ｐ（ｘ，ｙ）是Ｒｎ×Ｒｎ中的实值多项式，Ω是零次齐次函数且满足ｍ阶消失性条件，Ｒｍ＋１（Ａ；ｘ，ｙ）＝Ａ（ｘ）－｜α｜ｍＤαＡ（ｙ）（ｘ－ｙ）α，对任何｜α｜＝ｍ，ＤαＡ∈ＢＭＯ（Ｒｎ）．证明了Ω∈Ｌｑ（Ｓｎ－１）且ｑ＞１时，对任何１＜ｐ＜∞，‖ＴＡｆ‖ｐＣ（ｎ，ｍ，ｐ，ｄｅｇＰ）｜α｜＝ｍ‖ＤαＡ‖ＢＭＯ‖ｆ‖ｐ     

Boundedness of the Maximal Operator on Lipα（R^n）（0〈α〈1）

§１．　ＩｎｔｒｏｄｕｃｔｉｏｎＡｌｏｃａｌｌｙｉｎｔｅｇｒａｂｌｅｆｕｎｃｔｉｏｎｆ（ｘ）ｂｅｌｏｎｇｓｔｏＬｉｐα（Ｒｎ）,ｉｆｔｈｅｒｅｉｓａｃｏｎｓｔａｎｔＣ,ｓｕｃｈｔｈａｔｆｏｒｅｖｅｒｙｘ,ｙ∈Ｒｎ｜ｆ（ｘ）－ｆ（ｙ）｜≤Ｃ｜ｘ－ｙ｜α　　ＴｈｅｓｍａｌｌｅｓｔｃｏｎｓｔａｎｔＣｓａｔｉｓｆｉｅｓａｂｏｖｅｉｓｃａｌｌｅｄＬｉｐｓｃｈｉｔｚｎｏｒｍｏｆｆａｎｄｉｓｄｅｎｏｔｅｄｂｙｙｆｙ∧α．Ｂｙ［１］,ｆ∈Ｌｉｐα（Ｒｎ）ｅｑｕｉｖａｌｅｎｔｔｏｆ∈εα,２,ｗｈｅｒｅεα,２＝…     

Banach空间上凸函数的Gateaux可微点

Ｂａｎａｃｈ空间上凸函数的Ｇａｔｅａｕｘ可微点杨富春云南大学本文总设定Ｘ是实Ｂａｎａｃｈ空间，Ｄ是ｘ的非空开凸子集，ｊ：Ｄ→Ｒ是连续的凸函数。ｆ在ｘ∈Ｄ的Ｇａｔｅａｕｘ导数，简称Ｇ导数。记在ｘ点６可微若则记为ｆ在ｘ∈Ｇ（ｆ；Ｄ）的Ｇ导数｝。已经知道，...     

Solving generalized equations via homotopies

Global Newton methods for computing solutions of nonlinear systems of equations have recently received a great deal of attention. By using the theory of generalized equations, a homotopy method is proposed to solve problems arising in complementarity and mathematical programming, as well as in variational inequalities. We introduce the concepts of generalized homotopies and regular values, characterize the solution sets of such generalized homotopies and prove, under boundary conditions similar to Smale’s [10], the existence of a homotopy path which contains an odd number of solutions to the problem. We related our homotopy path to the Newton method for generalized equations developed by Josephy [3]. An interpretation of our results for the nonlinear programming problem will be given.     

关于广义Newton法的收敛性问题

本文在较弱的条件下,证明了Ｂ－可微方程组的广义Ｎｅｗｔｏｎ法的局部超线性收敛性,为该算法直接应用于非线性规划问题、变分不等问题以及非线性互补问题等提供了理论依据。最后,本文给出了广义Ｎｅｗｔｏｎ法付之实践的具体策略。数值结果表明,算法是行之有效的。     

A generalized Jacobian based Newton method for semismooth block-triangular system of equations

This paper will consider the problem of solving the nonlinear system of equations with block-triangular structure. A generalized block Newton method for semismooth sparse system is presented and a locally superlinear convergence is proved. Moreover, locally linear convergence of some parameterized Newton method is shown.     

A Generalized Univariate Newton Method Motivated by Proximal Regularization

We devise a new generalized univariate Newton method for solving nonlinear equations, motivated by Bregman distances and proximal regularization of optimization problems. We prove quadratic convergence of the new method, a special instance of which is the classical Newton method. We illustrate the possible benefits of the new method over the classical Newton method by means of test problems involving the Lambert W function, Kullback?CLeibler distance, and a polynomial. These test problems provide insight as to which instance of the generalized method could be chosen for a given nonlinear equation. Finally, we derive a closed-form expression for the asymptotic error constant of the generalized method and make further comparisons involving this constant.     

Convergence of a generalized Newton and an inexact generalized Newton algorithms for solving nonlinear equations with nondifferentiable terms

In this paper, we consider two versions of the Newton-type method for solving a nonlinear equations with nondifferentiable terms, which uses as iteration matrices, any matrix from B-differential of semismooth terms. Local and global convergence theorems for the generalized Newton and inexact generalized Newton method are proved. Linear convergence of the algorithms is obtained under very mild assumptions. The superlinear convergence holds under some conditions imposed on both terms of equation. Some numerical results indicate that both algorithms works quite well in practice.      

On the Newton Interior-Point Method for Nonlinear Programming Problems

Interior-point methods have been developed largely for nonlinear programming problems. In this paper, we generalize the global Newton interior-point method introduced in Ref. 1 and we establish a global convergence theory for it, under the same assumptions as those stated in Ref. 1. The generalized algorithm gives the possibility of choosing different descent directions for a merit function so that difficulties due to small steplength for the perturbed Newton direction can be avoided. The particular choice of the perturbation enables us to interpret the generalized method as an inexact Newton method. Also, we suggest a more general criterion for backtracking, which is useful when the perturbed Newton system is not solved exactly. We include numerical experimentation on discrete optimal control problems.     

On an Augmented Lagrangian SQP Method for a Class of Optimal Control Problems in Banach Spaces

An augmented Lagrangian SQP method is discussed for a class of nonlinear optimal control problems in Banach spaces with constraints on the control. The convergence of the method is investigated by its equivalence with the generalized Newton method for the optimality system of the augmented optimal control problem. The method is shown to be quadratically convergent, if the optimality system of the standard non-augmented SQP method is strongly regular in the sense of Robinson. This result is applied to a test problem for the heat equation with Stefan-Boltzmann boundary condition. The numerical tests confirm the theoretical results.     

An algorithm for a class of nonlinear complementarity problems with non-Lipschitzian functions

In this paper, we focus on solving a class of nonlinear complementarity problems with non-Lipschitzian functions. We first introduce a generalized class of smoothing functions for the plus function. By combining it with Robinson's normal equation, we reformulate the complementarity problem as a family of parameterized smoothing equations. Then, a smoothing Newton method combined with a new nonmonotone line search scheme is employed to compute a solution of the smoothing equations. The global and local superlinear convergence of the proposed method is proved under mild assumptions. Preliminary numerical results obtained applying the proposed approach to nonlinear complementarity problems arising in free boundary problems are reported. They show that the smoothing function and the nonmonotone line search scheme proposed in this paper are effective.     

解约束优化的分段线性有理NCP函数

本文给出新的NCP函数,这些函数是分段线性有理正则伪光滑的,且具有良好的性质.把这些NCP函数应用到解非线性优化问题的方法中.例如,把求解非线性约束优化问题的KKT点问题分别用QP-free方法,乘子法转化为解半光滑方程组或无约束优化问题.然后再考虑用非精确牛顿法或者拟牛顿法来解决该半光滑方程组或无约束优化问题.这个方法是可实现的,且具有全局收敛性.可以证明在一定假设条件下,该算法具有局部超线性收敛性.     

Finite termination of a Newton-type algorithm for a class of affine variational inequality problems

Many optimization problems can be reformulated as a system of equations. One may use the generalized Newton method or the smoothing Newton method to solve the reformulated equations so that a solution of the original problem can be found. Such methods have been powerful tools to solve many optimization problems in the literature. In this paper, we propose a Newton-type algorithm for solving a class of monotone affine variational inequality problems (AVIPs for short). In the proposed algorithm, the techniques based on both the generalized Newton method and the smoothing Newton method are used. In particular, we show that the algorithm can find an exact solution of the AVIP in a finite number of iterations under an assumption that the solution set of the AVIP is nonempty. Preliminary numerical results are reported.