结合Armijo步长搜索的一类新的记忆梯度算法及其收敛特征

对求解无约束规划的超记忆梯度算法中线搜索方向中的参数,给了一个假设条件,从而确定了它的一个新的取值范围,保证了搜索方向是目标函数的充分下降方向,由此提出了一类新的记忆梯度算法.在去掉迭代点列有界和Armijo步长搜索下,讨论了算法的全局收敛性,且给出了结合形如共轭梯度法FR,PR,HS的记忆梯度法的修正形式.数值实验表明,新算法比Armijo线搜索下的FR、PR、HS共轭梯度法和超记忆梯度法更稳定、更有效.     

初始点任意的解非线性不等式约束优化问题的结合共轭梯度参数的超记忆梯度广义投影算法

本文利用广义投影矩阵，对求解无约束规划的超记忆梯度算法中的参数给出一种新的取值范围以保证得到目标函数的超记忆梯度广义投影下降方向，并与处理任意初始点的方法技巧结合建立求解非线性不等式约束优化问题的一个初始点任意的超记忆梯度广义投影算法，在较弱条件下证明了算法的收敛性．同时给出结合FR，PR，HS共轭梯度参数的超记忆梯度广义投影算法，从而将经典的共轭梯度法推广用于求解约束规划问题．数值例子表明算法是有效的．     

一类新的多步曲线搜索下的超记忆梯度法

研究一类新的求解无约束优化问题的超记忆梯度法,分析了算法的全局收敛性和线性收敛速率.算法利用一种多步曲线搜索准则产生新的迭代点,在每步迭代时同时确定下降方向和步长,并且不用计算和存储矩阵,适于求解大规模优化问题.数值试验表明算法是有效的.     

基于最佳超收敛阶EEP法的一维有限元自适应求解

基于新近提出的具有最佳超收敛阶的单元能量投影(EEP)超收敛算法,提出用具有最佳超收敛阶的EEP超收敛解对有限元解进行误差估计,用均差法进行网格划分,用拟有限元解进行多次遍历而不反复求解有限元真解,形成一套新型的一维有限元自适应求解策略.该法理论上简明清晰,算法上高效可靠,对于大多数问题,一步自适应迭代便可给出按最大模度量逐点满足误差限的有限元解答.以二阶椭圓型常微分方程模型问题为例,介绍了该法的基本思想、实施策略及具体算法,并给出具有代表性的数值算例,以展示该法的优良性能和效果.     

旅行商问题最优路径的改进免疫遗传算法

本文研究了一种改进的求解旅行商问题最优路径的免疫遗传算法.结合随机法与贪心法生成初始种群,利用亲和度排序而选取抗体以得到复制群体,引入轮盘赌及克隆选择获取高亲和度抗体,并实施疫苗接种及免疫记忆更新抗体.运用免疫记忆机理的闭环逻辑,证明了该算法生成的城市序列是全局收敛的.数值实验证明该算法是有效的.     

基于信赖域技术的非单调超记忆梯度算法

基于信赖域技术和修正拟牛顿方程,结合Zhang H.C.非单调策略,设计了新的求解无约束最优化问题的非单调超记忆梯度算法,分析了算法的收敛性和收敛速度.数值实验表明算法是有效的,适于求解大规模问题.     

一类变尺度算法的收敛性质

本文讨论一类变尺度算法的收敛性质,在一定条件下,证明了 Huang 算法类、吴方和桂湘云算法类及 Flachs 算法类的收敛性与超线性收敛性.特别,还证明了一类带有非精确线性搜索的算法之收敛性与超线性收敛性.     

一个基于定步长技术的超记忆梯度法

基于定步长技术,本文给出一种求解无约束优化问题的超记忆梯度算法,从而避免每步都执行线搜索.在一定条件下证明该算法具有全局收敛性和局部线性收敛率.由于该方法不用计算和存储矩阵,故适合于求解大规模优化问题.数值试验表明该算法是有效的.     

结合广义Armijo步长搜索的超记忆梯度算法及其收敛特征

对无约束规划 ( P) :minx∈ Rnf ( x) ,其中 f ( x)是 Rn→ R1上的一阶连续可微函数 ,设计了一个超记忆梯度求解算法 ,并在去掉迭代点列 { xk}有界和广义 Armijo步长搜索下 ,讨论了算法的全局的收敛性 ,证明了算法具有较强的收敛性质     

一类无需线性搜索的记忆梯度法

提出一类新的求解无约束优化问题的记忆梯度法,证明了算法的全局收敛性.当目标函数为一致凸函数时,对其线性收敛速率进行了分析.新算法在迭代过程中无需对步长进行线性搜索,仅需对算法中的一些参数进行预测估计,从而减少了目标函数及梯度的迭代次数,降低了算法的计算量和存储量.数值试验表明算法是有效的.     

A Family of Supermemory Gradient Projection Methods for Constrained Optimization

A family of supermemory gradient projection methods for solving the convex constrained optimization problem is presented in this article. It is proven to have stronger convergence properties than the traditional gradient projection method. In particular, it is shown to be globally convergent if the objective function is convex.     

A supermemory gradient projection algorithm for optimization problem with nonlinear constraints

In this paper, by extending concept of the supermemory gradient method for unconstrained optimization problems, we present a supermemory gradient projection algorithm for nonlinear programming with nonlinear constraints. Under some suitable conditions we prove its global convergence.     

Supermemory descent methods for unconstrained minimization

The supermemory gradient method of Cragg and Levy (Ref. 1) and the quasi-Newton methods with memory considered by Wolfe (Ref. 4) are shown to be special cases of a more general class of methods for unconstrained minimization which will be called supermemory descent methods. A subclass of the supermemory descent methods is the class of supermemory quasi-Newton methods. To illustrate the numerical effectiveness of supermemory quasi-Newton methods, some numerical experience with one such method is reported.The authors are indebted to Dr. H. Y. Huang for his helpful criticism of this paper.     

A nonmonotone supermemory gradient algorithm for unconstrained optimization

This paper presents a nonmonotone supermemory gradient algorithm for unconstrained optimization problems. At each iteration, this proposed method sufficiently uses the previous multi-step iterative information and avoids the storage and computation of matrices associated with the Hessian of objective functions, thus it is suitable to solve large-scale optimization problems and can converge stably. Under some assumptions, the convergence properties of the proposed algorithm are analyzed. Numerical results are also reported to show the efficiency of this proposed method.     

A new supermemory gradient method for unconstrained optimization problems

This paper presents a new supermemory gradient method for unconstrained optimization problems. It can be regarded as a combination of ODE-based methods, line search and subspace techniques. The main characteristic of this method is that, at each iteration, a lower dimensional system of linear equations is solved only once to obtain a trial step, thus avoiding solving a quadratic trust region subproblem. Another is that when a trial step is not accepted, this proposed method generates an iterative point whose step-length satisfies Armijo line search rule, thus avoiding resolving linear system of equations. Under some reasonable assumptions, the method is proven to be globally convergent. Numerical results show the efficiency of this proposed method in practical computation.     

Convergence of supermemory gradient method

In this paper we consider the global convergence of a new supermemory gradient method for unconstrained optimization problems. New trust region radius is proposed to make the new method converge stably and averagely, and it will be suitable to solve large scale minimization problems. Some global convergence results are obtained under some mild conditions. Numerical results show that this new method is effective and stable in practical computation.     

Necessary Optimality Conditions and New Optimization Methods for Cubic Polynomial Optimization Problems with Mixed Variables

Multivariate cubic polynomial optimization problems, as a special case of the general polynomial optimization, have a lot of practical applications in real world. In this paper, some necessary local optimality conditions and some necessary global optimality conditions for cubic polynomial optimization problems with mixed variables are established. Then some local optimization methods, including weakly local optimization methods for general problems with mixed variables and strongly local optimization methods for cubic polynomial optimization problems with mixed variables, are proposed by exploiting these necessary local optimality conditions and necessary global optimality conditions. A global optimization method is proposed for cubic polynomial optimization problems by combining these local optimization methods together with some auxiliary functions. Some numerical examples are also given to illustrate that these approaches are very efficient.     

线性约束三次规划问题的全局最优性必要条件和最优化算法

讨论了带线性不等式约束三次规划问题的最优性条件和最优化算法.首先,讨论了带有线性不等式约束三次规划问题的全局最优性必要条件.然后,利用全局最优性必要条件,设计了解线性约束三次规划问题的一个新的局部最优化算法(强局部最优化算法).再利用辅助函数和所给出的新的局部最优化算法,设计了带有线性不等式约束三次规划问题的全局最优化算法.最后,数值算例说明给出的最优化算法是可行的、有效的.     

Using multi-objective evolutionary algorithms for single-objective optimization

In recent decades, several multi-objective evolutionary algorithms have been successfully applied to a wide variety of multi-objective optimization problems. Along the way, several new concepts, paradigms and methods have emerged. Additionally, some authors have claimed that the application of multi-objective approaches might be useful even in single-objective optimization. Thus, several guidelines for solving single-objective optimization problems using multi-objective methods have been proposed. This paper offers a survey of the main methods that allow the use of multi-objective schemes for single-objective optimization. In addition, several open topics and some possible paths of future work in this area are identified.