关于拉氏变换之微分性质的一点注记

反例说明拉氏变换的一条微分性质存在的错误,通过理论分析指出其问题所在,并给出改正。     

复合函数求偏导数法则的证明一般书中都有毛病

本文指出了一般书中求复合函数偏导数法则的证明中的一个普遍毛病,并提出了改正的方法     

线性规划对偶问题的一个注记

指出了线性规划对偶问题定义中的一个小漏洞,并作了改正     

网络RTK电离层误差研究现状分析

近年来,网络RTK电离层延迟改正成为高精度实时定位中最感兴趣的研究课题之一,对基准站电离层延迟改正和改正数内插算法两个方面的主要研究内容、研究方法和研究进展进行了讨论,并探讨了目前存在的问题及探索性解决方法.     

关于“变分原理中的几点注记”的注记

本文用不同方法更简单地证明了Aziz得到的关于积Hilbert,空间上抽象方程有适定解的充分条件,并且重证了这个充分条件也是必要条件,对于抽象方程的适定解我们用不同的证法对Lax-Milgram定理给出一个不同的表述形式;对于Lions证明的关于Hilbert空间上变分不等式的一个基本结果,指出了它在积Hilbert空间的特定条件下也是成立的;改正了[6]中的失误。     

差别矩阵约简的简化算法的使用误区

差别矩阵约简算法是粗集属性约简的重要方法,简化算法能省去生成、存储差别矩阵的中间环节,减少时空运算,是一种实用方法.指出简化算法使用中的常见错误,分析了错误原因,给出了改正方法.     

修正多重尺度法在求解圆薄板具有很大挠度问题时的应用

本利用修正的多重尺度法＾［１～２］重新研究固支圆薄板在均匀压力作用下，挠度很大时解的渐近性态。结果表明与钱伟长教授用首创的合成展形法求解该问题＾［３］的结果相一致，但较后更简捷。本结果还表明［４］中所指出［１～２］方法的局限性是非本质的，并改正［３］中一些计算错误。     

近地气层土壤热交换的数值计算方法探讨

本文应用辛卜生数值积分的方法求解热传导方程中的偏微分项,推导出计算土壤热交换中的两个重要物理量——土壤导温率和土壤热通量,从理论上分析了它与目前热平衡台站规范法(拉氏法)的异同。指出拉氏方法中对土壤容积热容量的不同假设,必定给计算结果带来较大的误差,而本文提出的这一方法就不存在这一问题;它的另一优点是在计算通过20厘米处的热通量时考虑了二阶微分项的作用,即温度的非线性作用且该方法有较大的适用范围。     

关于Y.H.Kim两篇论文的注记

指出并改正了Y.H.Kim两篇论文中关于某些不等式的错误和疏忽.     

关于破产概率函数的可微性的注

本文对风险理论中的经典风险过程和带干扰复合Poisson过程的破产概率函数的可微性进行了讨论,指出了过去在这个问题上存在的一些问题,并对Embrechts(1994)文献中有关绝对连续性和完全单调性问题的证明中错误予以改正。     

Approximating Networks and Extended Ritz Method for the Solution of Functional Optimization Problems

Functional optimization problems can be solved analytically only if special assumptions are verified; otherwise, approximations are needed. The approximate method that we propose is based on two steps. First, the decision functions are constrained to take on the structure of linear combinations of basis functions containing free parameters to be optimized (hence, this step can be considered as an extension to the Ritz method, for which fixed basis functions are used). Then, the functional optimization problem can be approximated by nonlinear programming problems. Linear combinations of basis functions are called approximating networks when they benefit from suitable density properties. We term such networks nonlinear (linear) approximating networks if their basis functions contain (do not contain) free parameters. For certain classes of d-variable functions to be approximated, nonlinear approximating networks may require a number of parameters increasing moderately with d, whereas linear approximating networks may be ruled out by the curse of dimensionality. Since the cost functions of the resulting nonlinear programming problems include complex averaging operations, we minimize such functions by stochastic approximation algorithms. As important special cases, we consider stochastic optimal control and estimation problems. Numerical examples show the effectiveness of the method in solving optimization problems stated in high-dimensional settings, involving for instance several tens of state variables.     

Improved smoothing Newton methods for symmetric cone complementarity problems

There recently has been much interest in smoothing Newton method for solving nonlinear complementarity problems. We extend such method to symmetric cone complementarity problems (SCCP). In this paper, we first investigate a one-parametric class of smoothing functions in the context of symmetric cones, which contains the Fischer–Burmeister smoothing function and the CHKS smoothing function as special cases. Then we propose a smoothing Newton method for the SCCP based on the one-parametric class of smoothing functions. For the proposed method, besides the classical step length, we provide a new step length and the global convergence is obtained. Finally, preliminary numerical results are reported, which show the effectiveness of the two step lengthes in the algorithm and provide efficient domains of the parameter for the complementarity problems.     

Fractional-Order Legendre Functions and Their Application to Solve Fractional Optimal Control of Systems Described by Integro-differential Equations

In this paper, we introduce a set of functions called fractional-order Legendre functions (FLFs) to obtain the numerical solution of optimal control problems subject to the linear and nonlinear fractional integro-differential equations. We consider the properties of these functions to construct the operational matrix of the fractional integration. Also, we achieved a general formulation for operational matrix of multiplication of these functions to solve the nonlinear problems for the first time. Then by using these matrices the mentioned fractional optimal control problem is reduced to a system of algebraic equations. In fact the functions of the problem are approximated by fractional-order Legendre functions with unknown coefficients in the constraint equations, performance index and conditions. Thus, a fractional optimal control problem converts to an optimization problem, which can then be solved numerically. The convergence of the method is discussed and finally, some numerical examples are presented to show the efficiency and accuracy of the method.     

Smooth and Semismooth Newton Methods for Constrained Approximation and Estimation

In the article, we show that the constrained L ₂ approximation problem, the positive polynomial interpolation, and the density estimation problems can all be reformulated as a system of smooth or semismooth equations by using Lagrange duality theory. The obtained equations contain integral functions of the same form. The differentiability or (strong) semismoothness of the integral functions and the Hölder continuity of the Jacobian of the integral function were investigated. Then a globalized Newton-type method for solving these problems was introduced. Global convergence and numerical tests for estimating probability density functions with wavelet basis were also given. The research in this article not only strengthened the theoretical results in literatures but also provided a possibility for solving the probability density function estimation problem by Newton-type method.     

Interpolation problems,extensions of symmetric operators and reproducing kernel spaces II

The aim of part I and this paper is to study interpolation problems for pairs of matrix functions of the extended Nevanlinna class using two different approaches and to make explicit the various links between them. In part I we considered the approach via the Kreîn-Langer theory of extensions of symmetric operators. In this paper we adapt Dym's method to solve interpolation problems by means of the de Branges theory of Hilbert spaces of analytic functions. We also show here how the two solution methods are connected.     

Stability of Liénard Equation

We consider a balance stability problem for the second order nonlinear differential equations of the Liénard type. Investigations are carried out by means of constant-sign Lyapunov functions for problems of stability, asymptotical stability (local and global), and instability. We implicitly formulate a method of construction of constant-sign functions suitable for solving problems of motion stability. Special attention is paid to a problem of non-asymptotical stability, where we demonstrate possibilities of new assertions that rely upon a usage of constant-sign Lyapunov functions.     

Basis problems for matrix valued functions

Basis problems for self-adjoint matrix valued functions are studied. We suggest a new and nonstandard method to solve basis problems both in finite and infinite dimensional spaces. Although many results in this paper are given for operator functions in infinite dimensional Hilbert spaces, but to demonstrate practicability of this method and to present a full solution of basis problems, in this paper we often restrict ourselves to matrix valued functions which generate Rayleigh systems on the n-dimensional complex space Cn. The suggested method is an improvement of an approach given recently in our paper [M. Hasanov, A class of nonlinear equations in Hilbert space and its applications to completeness problems, J. Math. Anal. Appl. 328 (2007) 1487-1494], which is based on the extension of the resolvent of a self-adjoint operator function to isolated eigenvalues and the properties of quadratic forms of the extended resolvent. This approach is especially useful for nonanalytic and nonsmooth operator functions when a suitable factorization formula fails to exist.     

A random coordinate descent algorithm for optimization problems with composite objective function and linear coupled constraints

In this paper we propose a variant of the random coordinate descent method for solving linearly constrained convex optimization problems with composite objective functions. If the smooth part of the objective function has Lipschitz continuous gradient, then we prove that our method obtains an ?-optimal solution in $\mathcal{O}(n^{2}/\epsilon)$ iterations, where n is the number of blocks. For the class of problems with cheap coordinate derivatives we show that the new method is faster than methods based on full-gradient information. Analysis for the rate of convergence in probability is also provided. For strongly convex functions our method converges linearly. Extensive numerical tests confirm that on very large problems, our method is much more numerically efficient than methods based on full gradient information.     

Augmented Lagrangian method for probabilistic optimization

We analyze nonlinear stochastic optimization problems with probabilistic constraints described by continuously differentiable non-convex functions. We describe the tangent and the normal cone to the level sets of the underlying probability function and provide new insight into their structure. Furthermore, we formulate fist order and second order conditions of optimality for these problems based on the notion of p-efficient points. We develop an augmented Lagrangian method for the case of discrete distribution functions. The method is based on progressive inner approximation of the level set of the probability function by generation of p-efficient points. Numerical experience is provided.     

An interactive fuzzy satisficing method for multiobjective stochastic linear programming problems through an expectation model

For decision making problems involving uncertainty, both stochastic programming as an optimization method based on the theory of probability and fuzzy programming representing the ambiguity by fuzzy concept have been developing in various ways. In this paper, we focus on multiobjective linear programming problems with random variable coefficients in objective functions and/or constraints. For such problems, as a fusion of these two approaches, after incorporating fuzzy goals of the decision maker for the objective functions, we propose an interactive fuzzy satisficing method for the expectation model to derive a satisficing solution for the decision maker. An illustrative numerical example is provided to demonstrate the feasibility of the proposed method.