A note on “A Continuous Approach to Nonlinear Integer Programming”

Ge and Huang (1989) proposed an approach to transform nonlinear integer programming problems into nonlinear global optimization problems, which are then solved by the filled function transformation method. The approach has recently attracted much attention. This note indicates that the formulae to determine a penalty parameter in two fundamental theorems are incorrect, and presents the corrected formulae and revised theorems.     

NUMERICAL APPROXIMATION OF THE SMOLUCHOWSKI EQUATION USING RADIAL BASIS FUNCTIONS

The goal of this paper is to present a numerical method for the Smoluchowski equation,a drift-diffusion equation on the sphere,arising in the modelling of particle dynamics.The numerical method uses radial basis functions(RBF).This is a relatively new approach,which has recently mainly been used for geophysical applications.For a simplified model problem we compare the RBF approach with a spectral method,i.e.the standard approach used in related physical applications.This comparison as well as our other accuracy studies show that RBF methods are an attractive alternative for these kind of models.     

A new filled function method for nonlinear equations

In this paper, a new global optimization approach based on the filled function method is proposed for solving box-constrained systems of nonlinear equations. We first convert the nonlinear system into an equivalent global optimization problem, and then propose a new filled function method to solve the converted global optimization problem. Several numerical examples are presented and solved by using different local minimization methods, which illustrate the efficiency of the present approach.     

An O(n 3 L) primal interior point algorithm for convex quadratic programming

We present a primal interior point method for convex quadratic programming which is based upon a logarithmic barrier function approach. This approach generates a sequence of problems, each of which is approximately solved by taking a single Newton step. It is shown that the method requires iterations and O(n ^3.5 L) arithmetic operations. By using modified Newton steps the number of arithmetic operations required by the algorithm can be reduced to O(n ³ L).This research was supported in part by NSF Grant DMS-85-12277 and ONR Contract N-00014-87-K0214. It was presented at the Meeting on Mathematische Optimierung, Mathematisches Forschungsinstitut, Oberwolfach, West Germany, January 3–9, 1988.     

参数设计的绝对偏差法

本文在望目特性下分析了参数设计中内表所用的指标,指出了现有一些指标中存在的某些不足之处. 提出了新的内表指标——平均绝对偏差.在实际操作时,又把该指标分解为两个调节指标:正偏差与负偏差.本文以稳压电源的参数设计为例详细地叙述了这个“绝对偏差法”的全过程.在多个例子上的使用结果表明,此方法简单、易行、所寻找的可控因子调节方向常常是有效的.从而可不同程度地减少试验次数,提高分析效率.     

求解最大团问题的D函数正则化方法

最大团问题是一个经典的组合优化问题.在Motzkin和Straus的二次规划模型基础上,给出一种求解该问题的D函数正则化算法.通过引进D函数可以改善问题的凸性.几个标准考题的计算结果表明,该算法稳定有效.     

A Class of Augmented Filled Functions

The filled function method is an effective approach to find the global minimizer. Two of the recently proposed filled functions are H(X) and L₂(X). Although their numerical behavior is acceptable, they are not defined everywhere. This paper proposes a class of augmented filled functions with improved analyticity. Issues covered in the presented work include: theoretical properties, convergence analysis, geometric interpretation, algorithms, and numerical experiments. The overall performance of the new approach is comparable to the recently proposed ones.     

Potential fields induced by point sources in assemblies of shells weakened with apertures

Well‐posed boundary‐value problems in multiply‐connected regions are targeted for some sets of two‐dimensional Laplace equations written in geographical coordinates on joint surfaces of revolution. Those are problems that simulate potential fields induced by point sources in joint perforated thin shell structures consist of fragments of different geometry. A semi‐analytical approach is proposed to accurately compute solutions of such problems. The approach is based on the matrix of Green's type formalism. The elements of required matrices of Green's type are obtained analytically and expressed in closed computer‐friendly form. This makes it possible to efficiently deal with the targeted class of problems. Copyright © 2014 John Wiley & Sons, Ltd.     

Level set evolution for boundary extraction based on a p-Laplace equation

This paper presents a new approach to boundary extraction. We represent the object boundary by a level set model that is embedded in several scalar functions. The motion of the dynamic interface is governed by a p-Laplace equation. Such level set models are flexible in handling complex topological changes and are concise in extracting object boundaries despite of deep depression. Furthermore, a relatively smooth evolution can be maintained without re-initialization. The cost of this method is moderate. The accuracy and efficiency of the proposed algorithm are illustrated by several numerical examples.     

A filled function method with one parameter for unconstrained global optimization

The filled function method is considered as an efficient approach to solve the global optimization problems. In this paper, a new filled function method is proposed. Its main idea is as follows: a new continuously differentiable filled function with only one parameter is constructed for unconstrained global optimization when a minimizer of the objective function is found, then a minimizer of the filled function will be found in a lower basin of the objective function, thereafter, a better minimizer of the objective function will be found. The above process is repeated until the global optimal solution is found. The numerical experiments show the efficiency of the proposed filled function method.     

一个新的填充函数

填充函数法是一种解无约束全局极小化问题的方法.这种方法的关键是构造填充函数,在已发表的文献中已经介绍了几种填充函数.在此介绍只含一个参数的填充函数,并且根据此填充函数提出了一种填充函数算法.给出了用这种填充函数法解几个测试问题的计算结果.     

A dynamic meshless method for the least squares problem with some noisy subdomains

The interpolation method by radial basis functions is used widely for solving scattered data approximation. However, sometimes it makes more sense to approximate the solution by least squares fit. This is especially true when the data are contaminated with noise. A meshfree method namely, meshless dynamic weighted least squares (MDWLS) method, is presented in this paper to solve least squares problems with noise. The MDWLS method by Gaussian radial basis function is proposed to fit scattered data with some noisy areas in the problem’s domain. Existence and uniqueness of a solution is proved. This method has one parameter which can adjusts the accuracy according to the size of noises. Another advantage of the developed method is that it can be applied to problems with nonregular geometrical domains. The new approach is applied for some problems in two dimensions and the obtained results confirm the accuracy and efficiency of the proposed method. The numerical experiments illustrate that our MDWLS method has better performance than the traditional least squares method in case of noisy data.     

Estimating Parameters of a Microsimulation Model for Breast Cancer Screening Using the Score Function Method

In developing decision-making models for the evaluation of medical procedures, the model parameters can be estimated by fitting the model to data observed in (randomized) trials. For complex models that are implemented by discrete event simulation (microsimulation) of individual life histories, the Score Function (SF) method can potentially be an appropriate approach for such estimation exercises. We test this approach for a microsimulation model for breast cancer screening that is fitted to data from the HIP randomized trial for early detection of breast cancer. Comparison of the parameter values estimated using the SF method and the analytical solution shows that method performs well on this simple model. The precision of the estimated parameter values depends (as expected) on the size of the sample of simulated life histories, and on the number of parameters estimated. Using analytical representations for parts of the microsimulation model can increase the precision of the estimated parameter values. Compared to the Nelder and Mead Simplex method which is often used in stochastic simulation because of its ease of implementation, the SF method is clearly more efficient (ratio computer time: precision of estimates). The additional analytical investment needed to implement the SF method in an (existing) simulation model may well be worth the effort.     

Using a factored dual in augmented Lagrangian methods for semidefinite programming

In the context of augmented Lagrangian approaches for solving semidefinite programming problems, we investigate the possibility of eliminating the positive semidefinite constraint on the dual matrix by employing a factorization. Hints on how to deal with the resulting unconstrained maximization of the augmented Lagrangian are given. We further use the approximate maximum of the augmented Lagrangian with the aim of improving the convergence rate of alternating direction augmented Lagrangian frameworks. Numerical results are reported, showing the benefits of the approach.     

A meshless local moving Kriging method for two-dimensional solids

An improved meshless local Petrov-Galerkin method (MLPG) for stress analysis of two-dimensional solids is presented in this paper. The MLPG method based on the moving least-squares approximation is one of the recent meshless approaches. However, accurate imposition of essential boundary conditions in the MLPG method often presents difficulties because the MLPG shape functions does not possess the Kronecker delta property. In order to eliminate this shortcoming, this approach uses the moving Kriging interpolation instead of the traditional moving least-square approximation to construct the MLPG shape functions, and then, the Heaviside step function is used as the test function over a local sub-domain. In this method, the essential boundary conditions can be enforced as the FEM, no domain integration is needed and only regular boundary integration is involved. In addition, the sensitivity of several important parameters of the present method is mainly studied and discussed. Comparing with the original meshless local Petrov-Galerkin method, the present method has simpler numerical procedures and lower computation cost. The effectiveness of the present method for two-dimensional solids problem is investigated by numerical examples in this paper.     

压缩感知中信号重构的极大熵方法(英文)

本文针对压缩感知理论中BP算法的l1最优化问题,构造了一种新的信号重构的极大熵方法.极大熵方法克服了l1最优化问题的非光滑性,同时根据同伦方法构造极大熵函数的最优解序列来逼近全局最优稀疏解.数值实验表明极大熵方法是十分有效的信号重构方法.     

A numerical technique for solving IHCPs using Tikhonov regularization method

This study is intended to provide a numerical algorithm for solving a one-dimensional inverse heat conduction problem. The given heat conduction equation, the boundary conditions, and the initial condition are presented in a dimensionless form. The numerical approach is developed based on the use of the solution to the auxiliary problem as a basis function. To regularize the resultant ill-conditioned linear system of equations, we apply the Tikhonov regularization method to obtain the stable numerical approximation to the solution.     

Stability and convergence of spectral radial point interpolation method locally applied on two‐dimensional pseudoparabolic equation

In this article, we study a spectral meshless radial point interpolation of pseudoparabolic equations in two spatial dimensions. Shape functions, which are constructed through point interpolation method using the radial basis functions, help us to treat problem locally with the aim of high‐order convergence rate. The time derivatives are approximated by the finite difference time‐stepping method. The stability and convergence of this meshless approach are discussed and theoretically proven. Numerical results are presented to illustrate the theoretical findings. © 2016 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 33: 724–741, 2017     

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