极小相关组的Matlab实现及其在编码中的应用

纠错码技术是通信技术中不可或缺的一项技术,并在当今的通信技术发展中占有很高的地位,线性分组码是常用的一种纠错码,其纠错能力的计算需要以线性分组码最小码距的计算为基础。向量组极小相关组的提出,引入了向量组的距的定义,为线性分组码纠错能力的计算提供了新思路,极小相关组的有效而且快速求解方法研究将是一个新课题,针对计算机通信科学发展的需求,基于遍历法的思想,运用Matlab软件编写程序求解向量组的极小相关组,并将程序简单应用于具体向量组极小相关组的计算以及线性分组码纠错能力的计算。     

高维Euler方程组的强松弛极限

利用能量积分, 讨论在初值充分小的情形下, 高维带有阻尼项的Euler方程组光滑解的整体存在性和强松弛极限, 得到了解的一致先验估计, 并证明当松弛时间趋于0时, 整体解的渐近行为由多孔渗流方程控制.     

Potts models with magnetic field: Arithmetic,geometry, and computation

We give a sheaf theoretic interpretation of Potts models with external magnetic field, in terms of constructible sheaves and their Euler characteristics. We show that the polynomial countability question for the hypersurfaces defined by the vanishing of the partition function is affected by changes in the magnetic field: elementary examples suffice to see non-polynomially countable cases that become polynomially countable after a perturbation of the magnetic field. The same recursive formula for the Grothendieck classes, under edge-doubling operations, holds as in the case without magnetic field, but the closed formulae for specific examples like banana graphs differ in the presence of magnetic field. We give examples of computation of the Euler characteristic with compact support, for the set of real zeros, and find a similar exponential growth with the size of the graph. This can be viewed as a measure of topological and algorithmic complexity. We also consider the computational complexity question for evaluations of the polynomial, and show both tractable and NP-hard examples, using dynamic programming.     

待定系数法在无理函数积分中的应用

在教学过程中通过对练习题: 的求解、分析、综合和归纳得: (1) 式中P_n(X)_9 Q_n(x)均为n次多项式,其中Q_n(x)的各项系数待定。 (2) 式中P_n(x)为n次多项式,f_(n-1)(x)为各项系数待定的(n-1)次多项式,μ_n为待定常数。     

Rank 2 Ample Vector Bundles on Some Smooth Rational Surfaces

Several classification results for ample vector bundles of rank 2 on Hirzebruch surfaces, and on Del Pezzo surfaces, are obtained. In particular, we classify rank 2 ample vector bundles with _c2 less than seven on Hirzebruch surfaces, and with _c2 less than four on Del Pezzo surfaces.     

An Improved Gradient Projection-based Decomposition Technique for Support Vector Machines

In this paper we propose some improvements to a recent decomposition technique for the large quadratic program arising in training support vector machines. As standard decomposition approaches, the technique we consider is based on the idea to optimize, at each iteration, a subset of the variables through the solution of a quadratic programming subproblem. The innovative features of this approach consist in using a very effective gradient projection method for the inner subproblems and a special rule for selecting the variables to be optimized at each step. These features allow to obtain promising performance by decomposing the problem into few large subproblems instead of many small subproblems as usually done by other decomposition schemes. We improve this technique by introducing a new inner solver and a simple strategy for reducing the computational cost of each iteration. We evaluate the effectiveness of these improvements by solving large-scale benchmark problems and by comparison with a widely used decomposition package.     

Multicriteria decision making under uncertainty

Formalization for problems of multicriteria decision making under uncertainty is constructed in terms of guaranteed and weak estimates. A relevant definition of the vector maximinimax value is given. Parameterization and approximation of maximum, minimax, and maximinimax values based on the inverse logical convolution are suggested. An application for multicommodity networks is considered. Received: December 13, 2000 / Accepted: August 21, 2001?Published online May 8, 2002