Prox-Regularization Methods for Generalized Fractional Programming

If a fractional program does not have a unique solution or the feasible set is unbounded, numerical difficulties can occur. By using a prox-regularization method that generates a sequence of auxiliary problems with unique solutions, these difficulties are avoided. Two regularization methods are introduced here. They are based on Dinkelbach-type algorithms for generalized fractional programming, but use a regularized parametric auxiliary problem. Convergence results and numerical examples are presented.     

Some Properties of Methods of Centers

Huard's method of centers is a method that solves constrained convex problems by means of unconstrained problems. In this paper we give some properties of this method, we analyse its convergence and rate of convergence and suggest some other variants and techniques to improve the speed of convergence.     

Generic Algorithm for Generalized Fractional Programming

We propose a unified framework to study various versions of Dinkelbach-type algorithms for solving the generalized fractional programming problem. Classical algorithms used carefully selected iterate points and incorporated subtle convergence proofs. Our generic algorithm, however, is natural and simple. Moreover, the convergence analysis can be carried out through geometric observations and fundamental properties of convex functions. Consequently, the classical results are either refined or strengthened with new insights.     

解约束优化问题的投影梯度型中心方法

本文提出了一种求解约束优化问题的新算法—投影梯度型中心方法．在连续可微和非退化的假设条件下，证明了其全局收敛性．本文算法计算简单且形式灵活．     

求广义线性比试和问题全局解的新方法

为确定广义线性比式和规划问题(GFP)的全局最优解,提出一个新的分支定界方法.在算法中,分支过程采用单纯形对分规则,且界的估计通过一些线性规划问题的求解完成.给出算法的收敛性证明.数值试验结果显示算法是有效可行的.     

Finite Difference/Collocation Method for Two-Dimensional Sub-Diffusion Equation with Generalized Time Fractional Derivative

In this paper, we propose a finite difference/collocation method for two-dimensional time fractional diffusion equation with generalized fractional operator. The main purpose of this paper is to design a high order numerical scheme for the new generalized time fractional diffusion equation. First, a finite difference approximation formula is derived for the generalized time fractional derivative, which is verified with order $2-\alpha$ $(0<\alpha<1)$. Then, collocation method is introduced for the two-dimensional space approximation. Unconditional stability of the scheme is proved. To make the method more efficient, the alternating direction implicit method is introduced to reduce the computational cost. At last, numerical experiments are carried out to verify the effectiveness of the scheme.     

An Exact Method for Fractional Goal Programming

Goal Programming with fractional objectives can be reduced to mathematical programming with a linear objective under linear and quadratic constraints, thus optimal solutions can be obtained by using existing Global Optimization techniques. However, only heuristic procedures are suggested in the literature on the field. In this note we explore the practical applicability of a recent algorithm for nonconvex quadratic programming with quadratic constraints for this problem. Encouraging computational experiences for randomly generated instances with up to 14 fractional objectives are presented.     

A Unified Monotonic Approach to Generalized Linear Fractional Programming

We present an efficient unified method for solving a wide class of generalized linear fractional programming problems. This class includes such problems as: optimizing (minimizing or maximizing) a pointwise maximum or pointwise minimum of a finite number of ratios of linear functions, optimizing a sum or product of such ratios, etc. – over a polytope. Our approach is based on the recently developed theory of monotonic optimization.     

Rates of convergence for a method of centers algorithm

Convergence of a method of centers algorithm for solving nonlinear programming problems is considered. The algorithm is defined so that the subproblems that must be solved during its execution may be solved by finite-step procedures. Conditions are given under which the algorithm generates sequences of feasible points and constraint multiplier vectors that have accumulation points satisfying the Fritz John or the Kuhn-Tucker optimality conditions. Under stronger assumptions, linear convergence rates are established for the sequences of objective function, constraint function, feasible point, and multiplier values.This work was supported in part by the National Aeronautics and Space Administration, Predoctoral Traineeship No. NsG(T)-117, and by the National Science Foundation, Grants No. GP-25081 and No. GK-32710.The author wishes to thank Donald M. Topkis for his valuable criticism of an earlier version of this paper and a referee for his helpful comments.     

Local Convergence of the Interior-Point Newton Method for General Nonlinear Programming

In this paper, a formulation for an interior-point Newton method of general nonlinear programming problems is presented. The formulation uses the Coleman-Li scaling matrix. The local convergence and the q-quadratic rate of convergence for the method are established under the standard assumptions of the Newton method for general nonlinear programming.     

应用模糊目标规划方法求解多目标线性分式规划问题

针对多目标分式线性规划问题,提出利用上(下)界表示目标期望水平及允许上(下)限,且利用一阶泰勒公式逼近隶属函数,将多目标分式规划转化为线性规划问题,并用单纯形法求解,通过实验算例说明了所提出的方法的有效性.     

一种新的快速逼近真值的程序算法──分布误差法

本文介绍一种新的逼近真值的快速程序算法及其应用。     

线性二阶锥规划的一个光滑化方法及其收敛性

首先讨论了用Chen-Harker-Kanzow-Smale光滑函数刻画线性二阶锥规划的中心路径条件;基于此,提出了求解线性二阶锥规划的一个光滑化算法,然后分析了该算法的全局及其局部二次收敛性质.     

The Modified Upwind Finite Difference Fractional Steps Method for Compressible Two-phase Displacement Problem

For compressible two-phase displacement problem,the modified upwind finite difference fractionalsteps schemes are put forward.Some techniques,such as calculus of variations,commutative law of multiplicationof difference operators,decomposition of high order difference operators,the theory of prior estimates and tech-niques are used.Optimal order estimates in L~2 norm are derived for the error in the approximate solution.Thismethod has already been applied to the numerical simulation of seawater intrusion and migration-accumulationof oil resources.     

Globally Convergent Inexact Generalized Newton Method for First-Order Differentiable Optimization Problems

Motivated by the method of Martinez and Qi (Ref. 1), we propose in this paper a globally convergent inexact generalized Newton method to solve unconstrained optimization problems in which the objective functions have Lipschitz continuous gradient functions, but are not twice differentiable. This method is implementable, globally convergent, and produces monotonically decreasing function values. We prove that the method has locally superlinear convergence or even quadratic convergence rate under some mild conditions, which do not assume the convexity of the functions.     

A Combined Algorithm for Fractional Programming

In this paper, we present an outer approximation algorithm for solving the following problem: max _xS {f(x)/g(x)}, where f(x)0 and g(x)>0 are d.c. (difference of convex) functions over a convex compact subset S of R ⁿ . Let ()=max _xS (f(x)–g(x)), then the problem is equivalent to finding out a solution of the equation ()=0. Though the monotonicity of () is well known, it is very time-consuming to solve the previous equation, because that maximizing (f(x)–g(x)) is very hard due to that maximizing a convex function over a convex set is NP-hard. To avoid such tactics, we give a transformation under which both the objective and the feasible region turn to be d.c. After discussing some properties, we propose a global optimization approach to find an optimal solution for the encountered problem.     

Approximate Solutions and Duality Theorems for Continuous-Time Linear Fractional Programming Problems

This article proposes a practical computational procedure to solve a class of continuous-time linear fractional programming problems by designing a discretized problem. Using the optimal solutions of proposed discretized problems, we construct a sequence of feasible solutions of continuous-time linear fractional programming problem and show that there exists a subsequence that converges weakly to a desired optimal solution. We also establish an estimate of the error bound. Finally, we provide two numerical examples to demonstrate the usefulness of this practical algorithm.     

加热下分数阶广义二阶流体的Rayleigh-Stokes问题的一种有效数值方法

考虑加热下分数阶广义二阶流体的Rayleigh-Stokes问题(RSP-HGSGF),提出了一种逼近有界区域内RSP-HGSGF的有效数值方法.并且讨论了所提出方法的稳定性和收敛性.最后,利用数值例子体现数值方法的有效性.     

A Linearized Spectral-Galerkin Method for Three-Dimensional Riesz-Like Space Fractional Nonlinear Coupled Reaction-Diffusion Equations

In this paper, we establish a novel fractional model arising in the chemical reaction and develop an efficient spectral method for the three-dimensional Riesz-like space fractional nonlinear coupled reaction-diffusion equations. Based on the backward difference method for time stepping and the Legendre-Galerkin spectral method for space discretization, we construct a fully discrete numerical scheme which leads to a linear algebraic system. Then a direct method based on the matrix diagonalization approach is proposed to solve the linear algebraic system, where the cost of the algorithm is of a small multiple of $N^4$ ($N$ is the polynomial degree in each spatial coordinate) flops for each time level. In addition, the stability and convergence analysis are rigorously established. We obtain the optimal error estimate in space, and the results also show that the fully discrete scheme is unconditionally stable and convergent of order one in time. Furthermore, numerical experiments are presented to confirm the theoretical claims. As the applications of the proposed method, the fractional Gray-Scott model is solved to capture the pattern formation with an analysis of the properties of the fractional powers.     

多目标分式规划逆对偶研究

考虑了一类可微多目标分式规划问题.首先,建立原问题的两个对偶模型.随后,在相关文献的弱对偶定理基础上,利用Fritz John型必要条件,证明了相应的逆对偶定理.