多元弱样条空间和最小确定集

根据研究多元弱样条函数的B—网方法,给出了某些多元弱样条函数空间的最小确定集的构造方法,并从而求出了它们的维数．本文还讨论了对偶基的局部支集性质。     

关于高精度样条插值公式的一点注记

本文运用I.J.Schoenberg在(1)中建立的样条插值理论导出了一类低跨度的高精度样条插值公式。并将这类公式与[2]中的有关结果做了比较。     

用广义交互确认方法选择良好参数进行多元多项式散乱数据自然样条光顺

1．简介多元样条函数在多元逼近中发挥很大作用，已有数量相当多的综合报告和研究论文正式发表，就在1996年6月在法国召开的第三届国际曲线与曲面会议上便有不少多元样条方面的报告，不过总的感觉是仍然缺乏对噪声数据特别是散乱数据的有效光顺方法．李岳生、崔锦泰、关履泰、胡日章等讨论广义调配样条与张量积函数，并用希氏空间样条方法处理多元散乱数据样条插值与光顺，提出多元多项式自然样条，推广了相应一元的结果．我们知道，在样条光顺中有一个如何选择参数的问题，用广义交互确认方法（generalizedcross－validation，以下简称GC…     

特殊形式的多元有理样条插值

有理样条插值问题最早是由R.Schaback提出的,由于R.Schaback考虑此问题时涉及到了非线性方程组的求解,因而实现起来比较复杂.后来,王仁宏等研究了几类特殊形式的插值有理样条函数,避开了求解非线性方程的困难.能否在多元情形下建立类似的结果?本文对此作出了肯定的回答,并就二元情形的三角剖分和四边形剖分建立了几类特殊形式的插值多元有理样条,构造性地证明了解的存在性和唯一性.     

一种四次有理插值样条及其逼近性质

1引言有理样条函数是多项式样条函数的一种自然推广,但由于有理样条空间的复杂性,所以有关它的研究成果不象多项式样条那样完美,许多问题还值得进一步的研究．近几十年来,有理插值样条,特别是有理三次有理插值样条,由于它们在曲线曲面设计中的应用,已有许多学者进行了深入研究,取得了一系列的成果(见[1]-[7])．但四次有理插值样条由于其构造所花费的计算量太大以及在使用上很不方便而让人们忽视了其重要的应用价值,因此很少有人研究他们．实际上,在某些情况下四次有理插值样条有其独特的应用效果,如文[8]建立的一种具有局部插值性质的分母为二次的四次有理样条,即一个剖分     

一种广义周期Besov类的多元周期多项式样条逼近

本文证明多元多项式周期样条空间是某些多元周期光滑函数类的关于Kolmogorov n-宽度的弱渐近极子空间．给出了广义周期Besov类的一种推广，得到了空间元素的一种表示定理，不仅给出了一种多元周期多项式样条算子．而且证明了所得的结果．     

一类高维沙德意义下的最佳求积公式

Schoenberg,I.J.证明了由一元自然样条插值得到的求积公式和沙德意义下最佳求积公式是一致的。后者是指在具有同样代数精度的求积公式中其余项的皮亚诺核最小者。从而样条插值型求积公式是定积分在一定意义下的最佳逼近。李岳生教授提出了一类多元     

广义三次Hermite样条插值及其对振荡函数的积分等的数值逼近

本文在等距分划上引入在似于文［１］的Ｉ型广义Ｈｅｒｍｌｉｅ样条插值，改进了Ⅱ型广义Ｈｅｒｍｉｔｅ样条．与文［１］比较，我们证明了改进后的Ⅱ型广义Ｈｅｒｍｉｔｅ样条插值的逼近精度得到了充分的提高．并利用这二种样条插值，讨论了对振荡积分，有限Ｆｏｕｒｉｅｒ积分等的数值逼近．     

研究多元样条的逐次分解法

本文在协调方程的基础上提出了研究多元样条的逐次分解法，并由此明了多元样条（包括多项式样条、有理样条乃至更一般的样条）在本质上是一个积分微分方和组的解。该方法具有以下优点：１）即可研究多项式样条，又可以研究有理样条乃至更一般的样条；２）即适用于三角剖分，双适用于直线剖分乃至更一般的代数曲线剖分；３）即能用于研究样条空间，又能用于研究样条环；４）可使许多问题局部化。     

二元三方向剖分中B样条的B网结构与递推算法

§1.引言众所周知,de Boor-Con递推公式及微分-差分公式对于一元B样条的理论和应用极为重要。在多元样条中是否存在类似的结果,已成为近年来的研究课题。本文从B网结构出发,讨论三向剖分下不同次数样条空间的B样条之间的递推关系,指出不能简单地把函数形式的de Boor-Con公式搬到这里,然而可以在B网意义下实现递推。与一     

Constrained trace-optimization of polynomials in freely noncommuting variables

The study of matrix inequalities in a dimension-free setting is in the realm of free real algebraic geometry. In this paper we investigate constrained trace and eigenvalue optimization of noncommutative polynomials. We present Lasserre’s relaxation scheme for trace optimization based on semidefinite programming (SDP) and demonstrate its convergence properties. Finite convergence of this relaxation scheme is governed by flatness, i.e., a rank-preserving property for associated dual SDPs. If flatness is observed, then optimizers can be extracted using the Gelfand–Naimark–Segal construction and the Artin–Wedderburn theory verifying exactness of the relaxation. To enforce flatness we employ a noncommutative version of the randomization technique championed by Nie. The implementation of these procedures in our computer algebra system NCSOStoolsis presented and several examples are given to illustrate our results.     

Abel maps and limit linear series

We explore the relationship between limit linear series and fibers of Abel maps in the case of curves with two smooth components glued at a single node. To an \(r\)-dimensional limit linear series satisfying a certain exactness property (weaker than the refinedness property of Eisenbud and Harris) we associate a closed subscheme of the appropriate fiber of the Abel map. We then describe this closed subscheme explicitly, computing its Hilbert polynomial and showing that it is Cohen–Macaulay of pure dimension \(r\). We show that this construction is also compatible with one-parameter smoothings.     

Augmented Lagrangian Objective Penalty Function

Augmented Lagrangian function is one of the most important tools used in solving some constrained optimization problems. In this article, we study an augmented Lagrangian objective penalty function and a modified augmented Lagrangian objective penalty function for inequality constrained optimization problems. First, we prove the dual properties of the augmented Lagrangian objective penalty function, which are at least as good as the traditional Lagrangian function's. Under some conditions, the saddle point of the augmented Lagrangian objective penalty function satisfies the first-order Karush-Kuhn-Tucker condition. This is especially so when the Karush-Kuhn-Tucker condition holds for convex programming of its saddle point existence. Second, we prove the dual properties of the modified augmented Lagrangian objective penalty function. For a global optimal solution, when the exactness of the modified augmented Lagrangian objective penalty function holds, its saddle point exists. The sufficient and necessary stability conditions used to determine whether the modified augmented Lagrangian objective penalty function is exact for a global solution is proved. Based on the modified augmented Lagrangian objective penalty function, an algorithm is developed to find a global solution to an inequality constrained optimization problem, and its global convergence is also proved under some conditions. Furthermore, the sufficient and necessary calmness condition on the exactness of the modified augmented Lagrangian objective penalty function is proved for a local solution. An algorithm is presented in finding a local solution, with its convergence proved under some conditions.     

PURE SUBMODULES AND TORSION FREE IMAGES OF COMPLETELY DECOMPOSABLE TORSION FREE MODULES

ABSTRACT

Prebalanced and precobalanced sequences play an important role in the investigation of Butler Modules. For Butler groups (modules over the integers), they are equivalent conditions. This is not the case for modules over integral domains in general. We investigate conditions when one type of exactness would imply the other. We show that for analytically unramified domains, the equivalence of prebalanced and precobalanced exactness will hold if and only if every maximal ideal has a unique maximal ideal lying over it in the domain's integral closure.     

Exactness and algorithm of an objective penalty function

Penalty function is an important tool in solving many constrained optimization problems in areas such as industrial design and management. In this paper, we study exactness and algorithm of an objective penalty function for inequality constrained optimization. In terms of exactness, this objective penalty function is at least as good as traditional exact penalty functions. Especially, in the case of a global solution, the exactness of the proposed objective penalty function shows a significant advantage. The sufficient and necessary stability condition used to determine whether the objective penalty function is exact for a global solution is proved. Based on the objective penalty function, an algorithm is developed for finding a global solution to an inequality constrained optimization problem and its global convergence is also proved under some conditions. Furthermore, the sufficient and necessary calmness condition on the exactness of the objective penalty function is proved for a local solution. An algorithm is presented in the paper in finding a local solution, with its convergence proved under some conditions. Finally, numerical experiments show that a satisfactory approximate optimal solution can be obtained by the proposed algorithm.     

An Arbitrary-Order Discrete de Rham Complex on Polyhedral Meshes: Exactness,Poincaré Inequalities,and Consistency

In this paper, we present a novel arbitrary-order discrete de Rham (DDR) complex on general polyhedral meshes based on the decomposition of polynomial spaces into ranges of vector calculus operators and complements linked to the spaces in the Koszul complex. The DDR complex is fully discrete, meaning that both the spaces and discrete calculus operators are replaced by discrete counterparts, and satisfies suitable exactness properties depending on the topology of the domain. In conjunction with bespoke discrete counterparts of \(\text {L}^2\)-products, it can be used to design schemes for partial differential equations that benefit from the exactness of the sequence but, unlike classical (e.g., Raviart–Thomas–Nédélec) finite elements, are nonconforming. We prove a complete panel of results for the analysis of such schemes: exactness properties, uniform Poincaré inequalities, as well as primal and adjoint consistency. We also show how this DDR complex enables the design of a numerical scheme for a magnetostatics problem, and use the aforementioned results to prove stability and optimal error estimates for this scheme.

     

排序法计算指数寿命型元件串联系统可靠性经典精确最优置信下限

对于样本点是离散的情况,可用对样本点排序的方法确定可靠性置信下限,排序有很多种,有L-P排序、序贯排序、极大似然估计排序、修正L-P排序等。本文提出一种具有直观合理性的新的排序方法,计算指数寿命型元件串联系统可靠性经典精确最优置信下限。     

ABSOLUTE STABLE HOMOTOPY FINITE ELEMENT METHODS FOR CIRCULAR ARCH PROBLEM AND ASYMPTOTIC EXACTNESS POSTERIORI ERROR ESTIMATE

In this paper, HFEM is proposed to investigate the circular arch problem. Optimal error estimates are derived, some superconvergence results are established, and an asymptotic exactness posteriori error estimator is presented. In contrast with the classical displacement variational method, the optimal convergence rate for displacement is uniform to the small parameter. In contrast with classical mixed finite element methods, our results are free of the strict restriction on h(the mesh size) which is preserved by all the previous papers. Furtheremore we introduce an asymptotic exactness posteriori error estimator based on a global superconvergence result which is discovered in this kind of problem for the first time.     

Gersten resolution with support

In the present paper, we generalize the Quillen presentation lemma. As an application, for a given functor with transfers, we prove the exactness of its Gersten complex with support.