ε-OPTIMAL SOLUTION FOR CONTINUOUS MINIMAX PROBLEMS WITH MAXIMUM-ENTROPY FUNCTION

This paper describes and explores a maximum-entropy approach to continuous minimax problem, which is applicable in many fields, such as transportation planning and game theory. It illustrates that the maximum entropy approcach has easy framework and proves that every accumulation of {x_k} generated by maximum-entropy programming is -optimal solution of initial continuous minimax problem. The paper also explains BFGS or TR method for it. Two numerical exam.ples for continuous minimax problem are given     

通过广义D-间隙函数求解变分不等式问题的全局收敛性和误差界估计

The variational inequality problem can be reformulated as an unconstrained minimization problem through the D-gap function. Recently,Peng proposed a hybrid Newton-type method for minimizing the D-gap function.In this paper,a modification with generalized D-gap function gαβ of the method proposed by Peng is presented.It is shown that the algorithm has nice global convergence.This result here have improved and generalized those in the literature.Moreover, when the parameter β is chosen in a certain interval, it is proved that the generalized D-gap function gαβ has bounded level sets for the strongly monotone VIP. An error bound estimation of the algorithm is obtained.     

一类半无限规划的一种渐近替代约束方法和收敛性

A class of constrained semi infinite minimax problem is transformed into a simpleconstrained problem, by means of discretization decoraposirion and maximum entropy method,making use of surrogate constraint, The paper deals with the convergence of this asymptotic aI-proach method.     

Error bounds of two smoothing approximations for semi-infinite minimax problems

In the paper we investigate smoothing method for solving semi-infinite minimax problems. Not like most of the literature in semi-infinite minimax problems which are concerned with the continuous time version（i.e., the one dimensional semi-infinite minimax problems）, the primary focus of this paper is on multi- dimensional semi-infinite minimax problems. The global error bounds of two smoothing approximations for the objective function are given and compared. It is proved that the smoothing approximation given in this paper can provide a better error bound than the existing one in literature.     

A Constrained Optimization Approach for LCP

In this paper, LCP is converted to an equivalent nonsmooth nonlinear equation system H(x,y) = 0 by using the famous NCP function-Fischer-Burmeister function. Note that some equations in H(x, y) = 0 are nonsmooth and nonlinear hence difficult to solve while the others are linear hence easy to solve. Then we further convert the nonlinear equation system H(x, y) = 0 to an optimization problem with linear equality constraints. After that we study the conditions under which the K-T points of the optimization problem are the solutions of the original LCP and propose a method to solve the optimization problem. In this algorithm, the search direction is obtained by solving a strict convex programming at each iterative point, However, our algorithm is essentially different from traditional SQP method. The global convergence of the method is proved under mild conditions. In addition, we can prove that the algorithm is convergent superlinearly under the conditions: M is P0 matrix and the limit point is a strict complementarity solution of LCP. Preliminary numerical experiments are reported with this method.     

AN ITERATIVE BEM FOR THE INVERSE PROBLEM OF DETECTING CORROSION IN A PIPE

In this paper, we consider an inverse problem of determining the corrosion occurring in an inaccessible interior part of a pipe from the measurements on the outer boundary. The problem is modelled by Laplace's equation with an unknowm term γ in the boundary condition on the inner boundary. Based on the Maz'ya iterative algorithm, a regularized BEM method is proposed for obtaining approximate solutions for this inverse problem. The numerical results show that our method can be easily realized and is quite effective.     

Robust designs for models with possible bias and correlated errors

This paper studies the model-robust design problem for general models with an unknown bias or contamination and the correlated errors. The true response function is assumed to be from a reproducing kernel Hilbert space and the errors are fitted by the qth order moving average process MA（q）, especially the MA（1） errors and the MA（2） errors. In both situations, design criteria are derived in terms of the average expected quadratic loss for the least squares estimation by using a minimax method. A case is studied and the orthogonality of the criteria is proved for this special response. The robustness of the design criteria is discussed through several numerical examples.     

A SUPERLINEARLY CONVERGENT TRUST REGION ALGORITHM FOR LC^1 CONSTRAINED OPTIMIZATION PROBLEMS

In this paper, a new trust region algorithm for nonlinear equality constrained LC^1 optimization problems is given. It obtains a search direction at each iteration not by solving a quadratic programming subproblem with a trust region bound, but by solving a system of linear equations. Since the computational complexity of a QP-Problem is in general much larger than that of a system of linear equations, this method proposed in this paper may reduce the computational complexity and hence improve computational efficiency. Furthermore, it is proved under appropriate assumptions that this algorithm is globally and super-linearly convergent to a solution of the original problem. Some numerical examples are reported, showing the proposed algorithm can be beneficial from a computational point of view.     

Boundary Identification for a Blast Furnace

In this paper,the authors discuss an inverse boundary problem for the axi- symmetric steady-state heat equation,which arises in monitoring the boundary corrosion for the blast-furnace.Measure temperature at some locations are used to identify the shape of the corrosion boundary. The numerical inversion is complicated and consuming since the wear-line varies during the process and the boundary in the heat problem is not fixed.The authors suggest a method that the unknown boundary can be represented by a given curve plus a small perturbation,then the equation can be solved with fixed boundary,and a lot of computing time will be saved. A method is given to solve the inverse problem by minimizing the sum of the squared residual at the measuring locations,in which the direct problems are solved by axi- symmetric fundamental solution method. The numerical results are in good agreement with test model data as well as industrial data,even in severe corrosion case.     

NEWTON-REGULARIZED METHOD BASED ON A BASIC FUNCTION FOR SOLVING THE INVERSE PROBLEM OF CONVECTION-DIFFUSION EQUATIONS

In this paper, a new iteration algorithm to solve the coefficient inverse problem is described by using a "basic function" which is specially defined and the idea of regularization. The method is simple and clear.The main advantage of the algorithm is that its computing cost is less than other current algorithms, such as PST and Purlerbalion Methods. Since it has uniform scheme, on the other hand, the method can be easily exleded to other kinds of inverse problems of different leal equations, multidimensional inverse problem and multiparameler inverse problems, etc.     

一类线性规划问题的区间调节熵算法

本文将熵函数的思想和区间分析相结合,构造了一类线性规划问题的区间调节熵算法,讨论了调节熵函数的区间扩张及其收敛阶,以及相关的区域删除检验原则,证明了算法的收敛性,给出了数值算例.理论与数值结果表明该方法是可靠和有效的.     

Minimax问题的调节熵函数法

本文提出了求解minimax问题的调节熵函数法,理论分析及数值结果均表明该方法比原熵函数法更优越.     

带约束条件的离散Minimax问题的区间极大熵方法

给出了求解带约束条件Ｍｉｎｉｍａｘ问题的区间极大熵方法以及相关的算法,从数值例子来看,此算法是非常有效的。     

调节熵函数法

１．引言 考虑如下极小极大问题这里ｆｉ（ｘ）是Ｒｎ中连续可微的函数,ｍ≥２是正整数（Ｐ）是一类比较典型的非光滑优化问题,是许多实际问题的数学模型．同时,线性规划的 Ｋａｒｍａｒｋａｒ标准型的对偶也是（Ｐ）的形式,光滑约束优化问题的一类重要罚函数法也是将问题化为类似（Ｐ）的形式．所以,如何有效地求解（Ｐ）,是一个重要问题．近些年发展起来的嫡函数法（或称凝聚函数法）是一种较新颖而实用的方法．它借助信息论中 Ｓｈａｎｎｏｎ熵的概念,推导出一族光滑的极大熵函数Ｆｐ（ｘ）,且Ｆｐ（ｘ）一致逼近要极小化的非光…     

New maximum entropy-based algorithm for structural design optimization

A new algorithm based on nonlinear transformation is proposed to improve the classical maximum entropy method and solve practical problems of reliability analysis. There are three steps in the new algorithm. Firstly, the performance function of reliability analysis is normalized, dividing by its value when each input is the mean value of the corresponding random variable. Then the nonlinear transformation of such normalized performance function is completed by using a monotonic nonlinear function with an adjustable parameter. Finally, the predictions of probability density function and/or the failure probability in reliability analysis are achieved by looking the result of transformation as a new form of performance function in the classical procedure of maximum entropy method in which the statistic moments are given through the univariate dimension reduction method. In the proposed method, the uncontrollable error of integration on the infinite interval is removed by transforming it into a bounded one. Three typical nonlinear transformation functions are studied and compared in the numerical examples. Comparing with results from Monte Carlo simulation, it is found that a proper choice of the adjustable parameter can lead to a better result of the prediction of failure probability. It is confirmed in the examples that result from the proposed method with the arctangent transformation function is better than the other transformation functions. The error of prediction of failure probability is controllable if the adjustable parameter is chosen in a given interval, but the suggested value of the adjustable parameter can only be given empirically.     

一般约束极大极小问题的一个有效的近似解法

一般约束极大极小问题的一个有效的近似解法唐焕文，张立卫，王云诚（大连理工大学应用数学系，辽宁，１１６０２４）．摘要＊从共扼的观点出发，导出了极大熵函数，给出了处理一般约束极大极小问题的一个有效的近似方法—极大熵方法，并在较弱的条件下，证明了算法的收敛...     

多目标minimax问题的极大熵逼近收敛性

本文利用极大熵逼近函数，展开了多目标ｍｉｎｉｍａｘ问题的逼近方法的研究，并讨论了该逼近方法的收敛性，所得结果是目前已有的结果进一步拓广．     

一类约束不可微优化问题的极大熵方法

1.引言 用极大熵原理可以有效地处理某些优化问题,一般迭代2—6次即可达到工程要求的精度。本文给出一类约束不可微优化问题的两种极大熵方法,推广了[1,2]的结果,并研制了计算程序。试算结果说明效果良好。进一步的结果在[4]中给出。 考虑下述问题: