有限维逼近无限维总极值的积分型方法

本文用有限维逼近无限维的方法来讨论函数空间中的总体最优化问题．我们给出了新的最优性条件和用变测度方法求得的有限维解逼近总体最优解的算法．对于有约柬问题，我们用不连续罚函数法把有约束问题化为无约束问题来求解．最后，我们通过一个具有非凸状态约束的最优控制问可题的实例来说明算法的有效性．     

无限维空间中总极值的有限维逼近

变分学、最优控制、微分对策等问题,要求考虑无限维空间中的总极值问题,但实际计算中只能得出有限维空间中的解。本文利用积分型总极值途径和变测度的思想,给出了最优性条件,算法及从有限维过渡到无限维的收敛性,最后还给出构造变测度序列的两个例子。     

Hammerstein型方程的Galerkin—Newton方法

1 引言 关于Hammerstein型方程的数值逼近方法,许多作者做了工作,例如[1]、[2]、[3]、[4]等,他们把无限维空间中的 Hammerstein型方程转化为有限维空间中的非线性 Hammer-stein型方程,在此基础上,[1]、[2]又用Newton型迭代方法对有限维空间中的非线性方程做了进一步地讨论.[5]中把Newton迭代方法与投影方法结合在一起,考虑了Hilbert空间中具有紧性的非线性算子的不动点问题的数值解法.本文把Galerkin有限维逼近方法与Newton迭代方法紧密结合,把无限维Banach空间中一类具有单调型算子的非线性Ham-merstein型方程的求解问题在迭代过程中化为有限维空间中的线性代数方程组求解.并证明了迭代序列超线性收敛于原方程的解,最后举例说明了这一方法的应用.     

无穷维系统中算子Riccati方程解的注记[英文]

本文采用正交投影技巧研究无穷维系统中算子Riccati方程的解,利用有限维空间中一序列来逼近该算子Riccati方程的解.并给出一个数值例子来说明我们的结论.     

无限维无限时区LQ问题的近似解

研讨一种求无限时区上无限维LQ问题的近似解的计算方法, 此方法基于适当的有限时区上有限维LQ问题族之解, 证明了当其时区长度与状态空间维数都趋于无穷时, 它们的二重强极限存在且就是原无限时区无限维问题的解.     

积分型变测度算法及其若干应用

本文对积分型变测度算法作进一步分析,并考虑该算法在区域变动算法,变测度算法的重心序列,无限维测度的有限维逼近等问题上的应用。     

求解无限维非线性互补问题的光滑化牛顿法

研究一类无限维非线性互补问题的光滑化牛顿法.借助于非线性互补函数,将无限维非线性互补问题转化为一个非光滑算子方程.构造光滑算子逼近非光滑算子,在光滑逼近算子满足方向可微相容性的条件下,证明了光滑化牛顿法具有超线性收敛性.     

有限维赋范空间至$C(\Om)$-的等

本文讨论了有限维赋范空间Ｘ至无限维Ｃ（Ω）的等距逼近问题，证明了当Ｘ不足任意ｌｎ＾∞（ｎ∈Ｎ）的子空间且Ω中含无究多个孤立点时，等距逼近问题不成立；在其它情形该问题都成立。     

非线性广义系统最优控制的最大值原理：无限维情形

§1.引言 对于无限维非线性最优控制问题,[1]—[3]在一定条件下证明了最大值原理。在有限维情形,[4]讨论了线性广义系统的二次型指标最优问题。关于有限维非线性广义系统的讨论见[5],[6]。而对于无限维非线性广义系统的最优控制问题,目前尚无讨论。本文利用Ekeland变分原理[7]—[10]和Fattorini引理,对具有一般目标泛函的无限维广义系统的最优控制问题给出了最大值原理。     

有限维赋范空间至C(Ω)-的等距逼近问题

本文讨论了有限维赋范空间Ｘ至无限维Ｃ（Ω）的等距逼近问题．证明了当Ｘ不是任意ｌ∞ｎ（ｎ∈Ｎ）的子空间且Ω中含无穷多个孤立点时，等距逼近问题不成立；在其它情形该问题都成立．     

Finite dimensional approximation to solutions of minimization problems in functional spaces

In this paper we consider minimization problems whose objectives are defined on functional spaces. The integral global optimization technique is applied to characterize a global minimum as the limit of a sequence of approximating solutions on finite dimensional subspaces. Necessary and sufficient optimality conditions are presented. A variable measure algorithm is proposed to find such approximating solutions. Examples are presented to illustrate the variable measure method     

Levitin–Polyak well-posedness by perturbations of split minimization problems

In this paper, we extend well-posedness notions to the split minimization problem which entails finding a solution of one minimization problem such that its image under a given bounded linear transformation is a solution of another minimization problem. We prove that the split minimization problem in the setting of finite-dimensional spaces is Levitin–Polyak well-posed by perturbations provided that its solution set is nonempty and bounded. We also extend well-posedness notions to the split inclusion problem. We show that the well-posedness of the split convex minimization problem is equivalent to the well-posedness of the equivalent split inclusion problem.     

Restricted linear constrained minimization of quadratic functionals

In this work, a linearly constrained minimization of a positive semidefinite quadratic functional is examined. We propose two different approaches to this problem. Our results are concerning infinite dimensional real Hilbert spaces, with a singular positive semidefinite operator related to the functional, and considering as constraint a singular operator. The difference between the proposed approaches for the minimization and previous work on this problem is that it is considered for all vectors belonging to the least squares solutions set, or to the vectors perpendicular to the kernel of the related operator or matrix.     

函数空间中总极值的R-收敛有限维逼近

应用测度序列R-收敛的新概念来描述函数空间中总极值问题解的有限维逼近,并利用变差积分途径来寻找这样的解.针对有约束问题,运用罚变差积分算法把所给问题转化为无约束问题,且给出一个非凸状态约束最优控制问题的数值例子以说明该算法的有效性.     

Shapes and measures

It is a well-known fact that a measurable set a shape can beconsidered as a measure; the aim of this work is to solve anoptimal-shape problem in such a way that it also answers thequestion of whether measures can be considered as shapes. Thispaper introduces a new method for solving problems of optimalshape design; by a process of embedding, the problem is replacedby another in which we seek to minimize a linear form over asubset of the product of two measure spaces defined by linearequalities. This minimization is global, and the theory allowsus to develop a computational method which enables us to findthe solution by finite-dimensional linear programming. The nearlyoptimal pair (C, dC) is obtained via the optimal pair of measuresby an approximation procedure. It is sometimes necessary toapply a standard minimization algorithm, because of some limitationsin the accuracy. Some examples are presented.     

Perturbation theory for abstract optimization problems

A general perturbation theory is given for optimization problems in locally convex, linear spaces. Neither differentiability of the constraints nor regularity of the solutions of the unperturbed problem are assumed. Without reference to a particular multiplier rule, multipliers of the unperturbed problem are defined and used for characterizing solutions of a perturbed problem. In case of differentiable constraints or finite-dimensional spaces, the results exceed those known so far.     

Attractors for partly dissipative lattice dynamic systems in weighted spaces

In this paper, we study the asymptotic behavior of solutions for the partly dissipative lattice dynamical systems in weighted spaces. We first establish the dynamic systems on infinite lattice, and then prove the existence of the global attractor in weighted spaces by the asymptotic compactness of the solutions. It is shown that the global attractors contain traveling waves. The upper semicontinuity of the global attractor is also considered by finite-dimensional approximations of attractors for the lattice systems.     

A Unified Approach to the Global Exactness of Penalty and Augmented Lagrangian Functions I: Parametric Exactness

In this two-part study, we develop a unified approach to the analysis of the global exactness of various penalty and augmented Lagrangian functions for constrained optimization problems in finite-dimensional spaces. This approach allows one to verify in a simple and straightforward manner whether a given penalty/augmented Lagrangian function is exact, i.e., whether the problem of unconstrained minimization of this function is equivalent (in some sense) to the original constrained problem, provided the penalty parameter is sufficiently large. Our approach is based on the so-called localization principle that reduces the study of global exactness to a local analysis of a chosen merit function near globally optimal solutions. In turn, such local analysis can be performed with the use of optimality conditions and constraint qualifications. In the first paper, we introduce the concept of global parametric exactness and derive the localization principle in the parametric form. With the use of this version of the localization principle, we recover existing simple, necessary, and sufficient conditions for the global exactness of linear penalty functions and for the existence of augmented Lagrange multipliers of Rockafellar–Wets’ augmented Lagrangian. We also present completely new necessary and sufficient conditions for the global exactness of general nonlinear penalty functions and for the global exactness of a continuously differentiable penalty function for nonlinear second-order cone programming problems. We briefly discuss how one can construct a continuously differentiable exact penalty function for nonlinear semidefinite programming problems as well.