Bhattacharyya bound of variances of unbiased estimators in nonregular cases

Summary Bhattachayya bound is generalized to nonregular cases when the support of the density depends on the parameter, while it is differentiable several times with respect to the parameter within the support. Some example is discussed, where it is shown that the bound is sharp. Research supported by NSF Grant MCS-8301409.     

Convergence and Error Bound for Perturbation of Linear Programs

In various penalty/smoothing approaches to solving a linear program, one regularizes the problem by adding to the linear cost function a separable nonlinear function multiplied by a small positive parameter. Popular choices of this nonlinear function include the quadratic function, the logarithm function, and the x ln(x)-entropy function. Furthermore, the solutions generated by such approaches may satisfy the linear constraints only inexactly and thus are optimal solutions of the regularized problem with a perturbed right-hand side. We give a general condition for such an optimal solution to converge to an optimal solution of the original problem as the perturbation parameter tends to zero. In the case where the nonlinear function is strictly convex, we further derive a local (error) bound on the distance from such an optimal solution to the limiting optimal solution of the original problem, expressed in terms of the perturbation parameter.     

求解非线性规划问题的一类对偶算法

本文提出了一类求解不等式约束非线性规划问题的构造性对偶算法,我们证明在适当的条件下,势函数的罚参数存在一个阀值,当罚参数小于这个阀值时,由这一方法所产生的序列局部收敛于问题的一个Kuhn-Tucker解,我们也建立了解的依赖于罚参数的误差上界,最后,我们给出了一个特残势函数的数值结果。     

A Class of penalty functions for optimization problema with bound constraints

In this paper we propose a new class of continuously differentiable globally exact penalty functions for the solution of minimization problems with simple bounds on some (all) of the variables. The penalty functions in this class fully exploit the structure of the problem and are easily computable. Furthermore we introduce a simple updating rule for the penalty parameter that can be used in conjunction with unconstrained minimization techniques to solve the original problem.     

On a generalization of an upper bound for the exponential function

With the introduction of a new parameter n, Kim recently generalized an upper bound for the exponential function that implies the inequality between the arithmetic and geometric means. In this paper, we answer some of Kim's conjectures about the inequalities between Kim's generalized upper bound and the original one. We also see the validity of Kim's generalization for some further negative values of x for the case in which the n is rational with both numerator and denominator odd. The range of its validity for negative x is investigated through the study of the zero distribution of a certain family of quadrinomials.     

非线性约束优化问题的一个修正 Lagrangian 算法

基于一个含有控制参数的修正Lagrangian函数,该文建立了一个求解非线性约束优化问题的修正Lagrangian算法.在一些适当的条件下,证明了控制参数存在一个阀值,当控制参数小于这一阀值时,由这一算法产生的序列解局部收敛于问题的Kuhn-Tucker点,并且建立了解的误差上界.最后给出一些约束优化问题的数值结果.     

威布尔分布或极值分布可靠度置信下限的回归表示

对于定数截尾样本，给出了基于极值分布的位置和尺度参数的最好线性无偏估计（BLUE），获得了威布尔分布的可靠度的点估计和置信限之间的回归模型，从而可由威布 尔可靠度的点估计根据回归方程得到可靠度的置信下限，省去了大量的用表，为实际工作者带来了极大的方便，计算结果表明，回归方程有很高的精度。     

Global error bound for convex inclusion problems

The existence of global error bound for convex inclusion problems is discussed in this paper, including pointwise global error bound and uniform global error bound. The existence of uniform global error bound has been carefully studied in Burke and Tseng (SIAM J. Optim. 6(2), 265–282, 1996) which unifies and extends many existing results. Our results on the uniform global error bound (see Theorem 3.2) generalize Theorem 9 in Burke and Tseng (1996) by weakening the constraint qualification and by widening the varying range of the parameter. As an application, the existence of global error bound for convex multifunctions is also discussed.     

A Trust Region SQP Algorithm for Equality Constrained Parameter Estimation with Simple Parameter Bounds

We describe a new algorithm for a class of parameter estimation problems, which are either unconstrained or have only equality constraints and bounds on parameters. Due to the presence of unobservable variables, parameter estimation problems may have non-unique solutions for these variables. These can also lead to singular or ill-conditioned Hessians and this may be responsible for slow or non-convergence of nonlinear programming (NLP) algorithms used to solve these problems. For this reason, we need an algorithm that leads to strong descent and converges to a stationary point. Our algorithm is based on Successive Quadratic Programming (SQP) and constrains the SQP steps in a trust region for global convergence. We consider the second-order information in three ways: quasi-Newton updates, Gauss-Newton approximation, and exact second derivatives, and we compare their performance. Finally, we provide results of tests of our algorithm on various problems from the CUTE and COPS sets.     

Meeting the Welch and Karystinos-Pados Bounds on DS-CDMA Binary Signature Sets

The Welch lower bound on the total-squared-correlation (TSC) of binary signature sets is loose for binary signature sets whose length L is not a multiple of 4. Recently Karystinos and Pados [6,7] developed new bounds that are better than the Welch bound in those cases, and showed how to achieve the bounds with modified Hadamard matrices except in a couple of cases. In this paper, we study the open cases.     

求解非线性方程组的L-M方法

本文研究了求解奇异非线性方程组的Levenberg-Marquardt方法的收敛性.利用选取新的迭代参数求解非线性方程组的L-M方法,获得点列的超线性收敛性和二阶收敛性,并把试验结果与文献[19,20]的结果进行了比较.     

Information Inequalities for the Bayes Risk for a Family of Non-Regular Distributions

For a family of non-regular distributions with a location parameter including the uniform and truncated distributions, the stochastic expansion of the Bayes estimator is given and the asymptotic lower bound for the Bayes risk is obtained and shown to be sharp. Some examples are also given.     

An algorithm for maximizing entropy subject to simple bounds

An unusual form of the maximum entropy problem is considered, that includes simple bound constraints on the Fourier coefficients of the required image, as well as nonnegativity conditions on the image intensities. The algorithm avoids mixing these constraints by introducing a parameter into the objective function that is adjusted by an outer iteration. For each parameter value an inner iteration solves a large optimization calculation, whose constraints are just the simple bounds, by a combination of the conjugate gradient procedure and an active set method. An important feature is the ability to make many changes to the active set at once. The outer iteration includes a test for inconsistency of all the given constraints. The algorithm is described, a proof of convergence is given, and there are some second-hand remarks on numerical results.     

一类高阶微分方程第二特征值的上界

本文考虑形如（－１）ｔＤｔ（ｐ（ｘ）Ｄｔｙ）＝λ（－Ｄ２）ｒｙ，ｘ∈（ａ，ｂ），Ｄｋｙ（ａ）＝Ｄｋｙ（ｂ）＝０，ｋ＝０，１，２，…，ｔ－１｛的第二特征值λ２的上界问题，得到了定理１和定理２，其中定理１的估计系数与［ａ，ｂ］无关，定理２的结果在一定条件下比定理１的好．     

Optimal locating arrays for at most two faults

Locating arrays are of interest in generating software test suites to cover all t-way component interactions and locate interaction faults in component-based systems.Recently,Tang,Colbourn and Yin made an investigation into optimal locating arrays in the case where a single fault is to be located.They pointed out that when two or more faults were considered,matters would become rather complicated.To handle those cases generally seems challenging,but is well worth further research.In this paper,we establish a lower bound on the size of locating arrays with at most two faults,and then prove that optimal locating arrays meeting this bound can be equivalently characterized in terms of orthogonal arrays with prescribed properties.Using this characterization,we develop a number of constructions of optimal locating arrays.Two infinite series of optimal locating arrays are then obtained.     

On a family of distributions attaining the Bhattacharyya bound

A family of distributions for which an unbiased estimator of a functiong(θ) of a real parameter θ can attain the second order Bhattacharyya lower bound is derived. Indeed, we obtain a necessary and sufficient condition for the attainment of the second order Bhattacharyya bound for a family of mixtures of distributions which belong to the exponential family. Furthermore, we give an example which does not satisfy this condition, but where the Bhattacharyya bound is attainable for a non-exponential family of distributions.     

Boundaries of Correlation Adjustment with Applications to Financial Risk Management

Abstract

In recent years, stress testing has become a regulatory requirement for risk assessment in financial institutions. To perform stress testing in multi-asset case, adjusting the correlation matrix to an extreme level is an important process. With a larger matrix, it is more difficult to choose the right correlation coefficients such that the newly adjusted correlation matrix is still valid. In this article, we present a systematic way to obtain the boundaries of a correlation matrix for both single stress and multiple stress cases, which can help determine how much the correlation should be adjusted in the first place.     

Non-semistable Arakelov bound and hyperelliptic Szpiro ratio for function fields

We prove a complex function field analogue of Szpiro's conjecture for hyperelliptic curves and some applications. The cases of function fields of positive characteristic and number fields are discussed briefly.

     

An optimality criterion for global quadratic optimization

In this paper we prove a sufficient condition that a strong local minimizer of a bounded quadratic program is the unique global minimizer. This sufficient condition can be verified computationally by solving a linear and a convex quadratic program and can be used as a quality test for local minimizers found by standard indefinite quadratic programming routines.Part of this work was done while the author was at the University of Wisconsin-Madison.     

广义非负与正极因子的乘法扰动界

本文在乘法扰动下研究了加权极分解的广义非负极因子与广义正极因子的扰动界,同时,作为特殊情形,也获得了广义极分解与极分解的非负极因子与正极因子的乘法扰动界.