并行技术在约束凸规划化问题的对偶算法中的应用

用 Rosen(196 1)的投影梯度的方法求解约束凸规划化问题的对偶问题 ,在计算投影梯度方向时 ,涉及求关于原始变量的最小化问题的最优解 .我们用并行梯度分布算法 (PGD)计算出这一极小化问题的近似解 ,证明近似解可以达到任何给定的精度 ,并说明当精度选取合适时 ,Rosen方法仍然是收敛的     

基于时域误差限的大规模系统自适应模型降阶

为满足解大规模动态系统常微分方程组对精度和速度权衡的要求,提出了一种基于误差限的大规模系统自适应模型降阶方法,其中方法的误差分析基于时域最大误差限,降阶方法基于SVD-Krylov子空间的方法.方法既考虑了算法的复杂性,又保证了算法的精度.通过对典型实例分析,结果表明该方法在给定相对误差限10~(-4)下得出的降阶阶数在不同频率下都能给出很好的近似精度,低频1~10Hz平均相对误差为1.1812×10~(-5),高频1~10GHz平均相对误差为5.6408×10~(-5),即在很宽的频率范围内都能满足精度要求.     

基于近似一阶信息的加速的bundle level算法

本文提出了四种加速的BL(bundle level)算法来分别求解凸光滑函数、强凸光滑函数的极小值问题和一类鞍点(saddle-point)问题.这些算法可以运用目标函数的近似的一阶信息来得到上述几类问题的近似解.本文重点研究了在一阶信息误差上界可自由选取和给定不变的两种情形下,所提出的算法中近似解能达到的最佳精度以及相应的迭代复杂度.     

基于L-S方法求解一类方程

基于Lyapunov-Schmidt方法求出给定方程的分岐方程,Newton迭代得到其在分岐点附近的近似非平凡解枝,得到了满意的结果.     

泊松方程及平面弹性问题有限元方法中求高阶导数的提取法

在许多有限元计算中经常在求得近似解后还要求得到近似的解的导数.如在弹性计算中,如何从计算得到的位移近似解较好地计算应力早已被研究多年.如果计算中包含直接对近似解求导数,必然会丧失部分精度,得不到满意的结果.特别,若近似解为分片常数函数,则根本无法从直接求导数得到应力的近似值.Babuska和 Miller提出了所谓“提取法”,即利用推导出来的提取公式来求解的导数的近似值,以得到与近似解本身同     

泊松方程及平面弹性问题有限元方法中求高阶导数的提取法

在许多有限元计算中经常在求得近似解后还要求得到近似的解的导数.如在弹性计算中,如何从计算得到的位移近似解较好地计算应力早已被研究多年.如果计算中包含直接对近似解求导数,必然会丧失部分精度,得不到满意的结果.特别,若近似解为分片常数函数,则根本无法从直接求导数得到应力的近似值.Babuska和 Miller提出了所谓“提取法”,即利用推导出来的提取公式来求解的导数的近似值,以得到与近似解本身同     

时滞Lokta-Volterra生态模型的同伦分析解法

研究了时滞Lolta-Volterra生态模型.利用同伦分析方法,得到了该模型解的近似展开式,并对近似解与数值解进行了比较.比较结果表明,近似解具有较高的精度,该方法用于生态模型研究可行.     

一类非线性双曲型发展方程的孤子解

研究了一类非线性发展方程.首先在无扰动情形下,利用待定函数和泛函同伦映射方法得到了非扰动发展方程的孤子精确解和扰动方程的任意次近似行波孤子解.接着引入一个同伦映射,并选取初始近似函数,再用同伦映射理论,依次求出非线性双曲型发展扰动方程孤子解的各次近似解析解.再利用摄动理论举例说明了用该方法得到的近似解析解的有效性和各次近似解的近似度.最后,简述了用同伦映射方法得到的近似解的意义,指出了用上述方法得到的各次近似解具有便于求解、精度高等优点.     

算子和右端都近似给定的第一类算子方程的广义Arcangeli方法

本文利用正则化方法解算子和右端都是近似给定的第一类算子方程,利用广义Arcangeli准则决定正则参数,给出正则解的收敛性和渐近收敛阶估计,以及算子为Fredholm积分算子时的正则解的一致收敛性。     

算子和右端都近似给定的第一类算子方程的Тихонов正则解的渐近阶估计

本文利用正则化方法求解算子和右端都是近似给定的第一类算子方程,给出一个选择正则参数的方法,并给出正则解的渐近阶估计。     

Existence and uniqueness of solution for quasi-equilibrium problems and fixed point problems on complete metric spaces with applications

In this paper, we presented new and important existence theorems of solution for quasi-equilibrium problems, and we show the uniqueness of its solution which is also a fixed point of some mapping. We also show that this unique solution can be obtained by Picard’s iteration method. We also get new minimax theorem, and existence theorems for common solution of fixed point and optimization problem on complete metric spaces. Our results are different from any existence theorems for quasi-equilibrium problems and minimax theorems in the literatures.     

利用平面上的黄金分割法求全局最优解

给出了无约束全局最优问题的一种解法 ,该方法是一维搜索中的 0 .61 8法的推广 ,不仅使其适用范围由一维扩展到平面上 ,并且将原方法只适用于单峰函数的局部搜索改进为可适用于多峰函数的全局最优解的搜索 .给出了收敛性证明 .本法突出的优点在于 :适用性强、算法简单、可以在任意精度内寻得最优解并且克服了以往直接解法所共有的要求大量计算机内存的缺点 .仿真结果表明算法是有效的 .     

一类两参数非线性反应扩散方程奇摄动问题的广义解

利用奇异摄动方法讨论了一类两参数广义奇摄动反应扩散方程问题.首先,在适当的条件下,对两个小参数进行幂级数展开,构造了问题的形式外部解.其次,在区域边界邻近,建立局部坐标系,利用多重尺度变量方法分别构造了问题解的第一、第二边界层校正项.最后,利用合成展开理论,得到了问题广义解的渐近表示式,并用泛函分析不动点原理,估计了渐近展开式的精度.该文得到问题的广义解在重叠区域内具有两个不同厚度的校正函数.它们分别对边界条件起着校正的作用,扩展了问题研究范围,同时还提供了构造这类在重叠区域上不同厚度的校正项的方法,因此具有广泛的研究前景.     

Minimizing Quadratic Functions Subject to Bound Constraints with the Rate of Convergence and Finite Termination

A new active set based algorithm is proposed that uses the conjugate gradient method to explore the face of the feasible region defined by the current iterate and the reduced gradient projection with the fixed steplength to expand the active set. The precision of approximate solutions of the auxiliary unconstrained problems is controlled by the norm of violation of the Karush-Kuhn-Tucker conditions at active constraints and the scalar product of the reduced gradient with the reduced gradient projection. The modifications were exploited to find the rate of convergence in terms of the spectral condition number of the Hessian matrix, to prove its finite termination property even for problems whose solution does not satisfy the strict complementarity condition, and to avoid any backtracking at the cost of evaluation of an upper bound for the spectral radius of the Hessian matrix. The performance of the algorithm is illustrated on solution of the inner obstacle problems. The result is an important ingredient in development of scalable algorithms for numerical solution of elliptic variational inequalities.     

The convergence of Jacobi–Davidson iterations for Hermitian eigenproblems

Rayleigh quotient iteration is an iterative method with some attractive convergence properties for finding (interior) eigenvalues of large sparse Hermitian matrices. However, the method requires the accurate (and, hence, often expensive) solution of a linear system in every iteration step. Unfortunately, replacing the exact solution with a cheaper approximation may destroy the convergence. The (Jacobi‐) Davidson correction equation can be seen as a solution for this problem. In this paper we deduce quantitative results to support this viewpoint and we relate it to other methods. This should make some of the experimental observations in practice more quantitative in the Hermitian case. Asymptotic convergence bounds are given for fixed preconditioners and for the special case if the correction equation is solved with some fixed relative residual precision. A dynamic tolerance is proposed and some numerical illustration is presented. Copyright © 2002 John Wiley & Sons, Ltd.     

Iterative refinement enhances the stability ofQR factorization methods for solving linear equations

Iterative refinement is a well-known technique for improving the quality of an approximate solution to a linear system. In the traditional usage residuals are computed in extended precision, but more recent work has shown that fixed precision is sufficient to yield benefits for stability. We extend existing results to show that fixed precision iterative refinement renders anarbitrary linear equations solver backward stable in a strong, componentwise sense, under suitable assumptions. Two particular applications involving theQR factorization are discussed in detail: solution of square linear systems and solution of least squares problems. In the former case we show that one step of iterative refinement suffices to produce a small componentwise relative backward error. Our results are weaker for the least squares problem, but again we find that iterative refinement improves a componentwise measure of backward stability. In particular, iterative refinement mitigates the effect of poor row scaling of the coefficient matrix, and so provides an alternative to the use of row interchanges in the HouseholderQR factorization. A further application of the results is described to fast methods for solving Vandermonde-like systems.     

Fixing variables and generating classical cutting planes when using an interior point branch and cut method to solve integer programming problems

Branch and cut methods for integer programming problems solve a sequence of linear programming problems. Traditionally, these linear programming relaxations have been solved using the simplex method. The reduced costs available at the optimal solution to a relaxation may make it possible to fix variables at zero or one. If the solution to a relaxation is fractional, additional constraints can be generated which cut off the solution to the relaxation, but donot cut off any feasible integer points. Gomory cutting planes and other classes of cutting planes are generated from the final tableau. In this paper, we consider using an interior point method to solve the linear programming relaxations. We show that it is still possible to generate Gomory cuts and other cuts without having to recreate a tableau, and we also show how variables can be fixed without using the optimal reduced costs. The procedures we develop do not require that the current relaxation be solved to optimality; this is useful for an interior point method because early termination of the current relaxation results in an improved starting point for the next relaxation.     

Relaxation-time limit and initial layer in the isentropic hydrodynamic model for semiconductors

This work is concerned with the relaxation-time limit in the multidimensional isentropic hydrodynamic model for semiconductors in the critical Besov space. Firstly, we construct formal approximations of the initial layer solution to the nonlinear problem by the matched expansion method. Then, assuming some regularity of the solution to the reduced problem, and proves the existence of classical solutions in the uniform time interval where the reduced problem has a smooth solution and justify the validity of the formal approximations in any fixed compact subset of the uniform time interval.     

病态线性方程组的判定方法

针对用条件数来衡量方程组的性态将随阶数增大而变得异常困难这一问题,分析了病态线性方程组产生的原因,提出了一种判定方法,探讨了对一定精度要求的解的可允许扰动的数量级,实例证明了这种方法的有效性.     

Cost Comparisons for Out-of-Phase Inventory Models

Inventory costs for a fixed time period have traditionally been determined by allocating total costs per cycle uniformly throughout that cycle as well as any partial cycles. This procedure for cost allocation has led to the solution of numerous inventory problems, most notable of which is the anticipated price-increase model. When comparing two out-of-phase inventory models, if costs are accounted for when they occur over a fixed planning horizon, inventory policies should be changed to reflect the impact of this different cost-allocation procedure. For the anticipated price-increase model, the ‘optimal’ order quantity as well as the implied savings in inventory costs will be different when cost models are developed based on these different cost-allocation methods. If the objective is to maximize over a fixed planning horizon the actual savings in inventory costs as they occur, the cost models presented here should be used.