求解广义混合隐拟平衡问题的预测修正算法

引入和研究了一类新的具有四元函数的广义混合隐拟平衡问题．这类平衡问题包含了很多已知的广义平衡问题和广义混合隐拟变分不等式问题作为很特殊的情形．利用辅助原理技巧建议和分析了求解广义混合隐拟平衡问题的预测修正迭代算法．所建议算法的收敛性仅需要映象的连续性和部分松弛强单调性．     

Banach空间中完全广义混合隐似平衡问题的辅助原理和迭代算法

在Banach空间中利用双线性连续泛函F代替内积引进了新的一类完全广义混合隐似平衡问题，引进了F强单调的概念，提出了该平衡问题的广义辅助问题，证明了广义辅助问题的收敛定理，给出了新的算法和由此算法产生的迭代序列的收敛特征．     

一般混合似变分不等式的隐式迭代算法

对一般混合似变分不等式的若干隐式迭代算法进行了研究;利用一般混合似变分不等式与不动点问题和预解方程的等价关系,采用分裂技巧和自适应迭代技巧结合,提出了一个求解一般混合似变分不等式的新的隐式迭代算法;并证明了该算法在算子T是g-单调连续的条件下收敛．     

局部FC-一致空间内凝聚映象的极大元和广义对策及应用(Ⅱ)

在局部FC-一致空间内,引入和研究了某些新的涉及凝聚集值映象的广义矢量拟平衡问题组．应用作者在文(Ⅰ)中对局部FC-一致空间内的凝聚集值映象得到的极大元存在性定理,在局部FC-一致空间内,对这些广义矢量拟平衡问题组的解,证明了某些新的存在性定理．这些结果改进和推广了文献中某些已知结果到局部FC-一致空间．     

一类混合似变分不等式问题解的存在唯一性

本文中,我们首先给出了一类混合似变分不等式问题．接着,在Banach空间中研究了它的解的存在性和唯一性．最后,讨论了混合似变分不等式问题的扰动问题,并证明了扰动问题的解的存在唯一性定理．     

Banach空间内一类广义混合隐平衡问题组解的存在性和迭代算法

在Banach空间内引入和研究了一类新的广义混合隐平衡问题组．首先,对广义混合隐平衡问题组引入了Yosida逼近映射概念．利用此概念,考虑了一个广义方程问题组并证明了它与广义混合隐平衡问题组的等价性．其次,应用广义方程问题组,建议和分析了计算广义混合隐平衡问题组的近似解的迭代算法．在相当温和的条件下,证明了由算法生成的迭代序列的强收敛性．这些结果是新的并且统一和推广了这一领域内的某些最近结果．     

一般多值混合隐拟变分不等式的解的存在性与算法

引入了实Hilbert空间中一类新的一般多值混合隐拟变分不等式.它概括了丁协平教授引入与研究过的熟知的广义混合隐拟变分不等式类成特例.运用辅助变分原理技巧来解这类一般多值混合隐拟变分不等式.首先,定义了具真凸下半连续的二元泛函的新的辅助变分不等式,并选取了一适当的泛函,使得其唯一的最小值点等价于此辅助变分不等式的解.其次,利用此辅助变分不等式,构造了用于计算一般多值混合隐拟变分不等式逼近解的新的迭代算法.在此,等价性保证了算法能够生成一列逼近解.最后,证明了一般多值混合隐拟变分不等式解的存在性与逼近解的收敛性.而且,给算法提供了新的收敛判据.因此,结果对M.A.Noor提出的公开问题给出了一个肯定答案,并推广和改进了关于各种变分不等式与补问题的早期与最近的结果,包括最近文献中涉及单值与集值映象的有关混合变分不等式、混合拟变不等式与拟补问题的相应结果.     

集值混合变分不等式的一类自适应惯性投影次梯度算法

本文在实Hilbert空间上引入了一类求解集值混合变分不等式新的自适应惯性投影次梯度算法.在集值映射T为f-强伪单调或单调的条件下,我们证明了由该自适应惯性投影次梯度算法所产生的序列强收敛于集值混合变分不等式问题的的唯一解.     

广义集值强非线性混合似变分不等式解的迭代逼近

延拓辅助原理的技巧研究一类取非紧值的集值映象的广义强非线性混合似变分不等式．证明了这类广义强非线性混合似变分不等式的辅助问题解的存在性．利用该存在性结果,给出了解这类广义强非线性混合似变分不等式的迭代算法,最终证明了这类广义强非线性混合似变分不等式解的存在性及由算法生成的迭代序列的收敛性．     

g-Eta-单调映像和解广义隐似变分包含的预解算子技巧

引入一类新的g-Eta-单调映像和一类涉及g-Eta-单调映像的广义隐似变分包含；定义了g-Eta-单调映像的预解算子，并证明了其Lipschitz连续性；分析和给出了这类涉及g-Eta-单调映像的广义隐似变分包含的迭代算法，并证明了其收敛性．     

A simulated annealing algorithm for resource constrained project scheduling problems

This paper presents a simulated annealing algorithm for resource constrained project scheduling problems with the objective of minimising makespan. In the search algorithm, a solution is represented with a priority list, a vector of numbers each of which denotes the priority of each activity. In the algorithm, a priority scheduling method is used for making a complete schedule from a given priority list (and hence a project schedule is defined by a priority list). The search algorithm is applied to find a priority list which corresponds to a good project schedule. Unlike most of priority scheduling methods, in the suggested algorithm some activities are delayed on purpose so as to extend search space. Solutions can be further improved by delaying certain activities, since non-delay schedules are not dominant in the problem (the set of non-delay schedules does not always include an optimal solution). The suggested algorithm is flexible in that it can be easily applied to problems with an objective function of a general form and/or complex constraints. The performance of the simulated annealing algorithm is compared with existing heuristics on problems prepared by Patterson and randomly generated test problems. Computational results showed that the suggested algorithm outperformed existing ones.     

A Hybrid Symbiosis Organisms Search algorithm and its application to real world problems

In this paper, a new hybrid algorithm, Hybrid Symbiosis Organisms Search (HSOS) has been proposed by combining Symbiosis Organisms Search (SOS) algorithm with Simple Quadratic Interpolation (SQI). The proposed algorithm provides more efficient behavior when dealing with real-world and large scale problems. To verify the performance of this suggested algorithm, 13 (Thirteen) well known benchmark functions, CEC2005 and CEC2010 special session on real-parameter optimization are being considered. The results obtained by the proposed method are compared with other state-of-the-art algorithms and it was observed that the suggested approach provides an effective and efficient solution in regards to the quality of the final result as well as the convergence rate. Moreover, the effect of the common controlling parameters of the algorithm, viz. population size, number of fitness evaluations (number of generations) of the algorithm are also being investigated by considering different population sizes and the number of fitness evaluations (number of generations). Finally, the method endorsed in this paper has been applied to two real life problems and it was inferred that the output of the proposed algorithm is satisfactory.     

A branch and bound algorithm for determining locations of long-term care facilities

This paper focuses on the problem of determining locations for long-term care facilities with the objective of balancing the numbers of patients assigned to the facilities. We present a branch and bound algorithm by developing dominance properties, a lower bounding scheme and a heuristic algorithm for obtaining an upper bound for the problem. For evaluation of the suggested branch and bound algorithm, computational experiments are performed on a number of test problems. Results of the experiments show that the suggested algorithm gives optimal solutions of problems of practical sizes in a reasonable amount of computation time.     

精确解析法的子结构算法

文[1]提出精确解析法,用以求解任意变系数常微分方程,并利用初参数算法给出一个解的解析表达式.但利用初参数算法,对某一类问题,如长柱壳弯曲和振动等,它们的解将难以在计算机上得到.本文通过非均匀轴对称长圆柱壳弯曲问题,给出精确解析法的子结构算法,它能够计算初参数算法在计算机上不能解决的问题.问题最后和初参数算法一样能归结为求解一个低阶代数方程组.文末给出算例,表明本文算法的正确性,并和初参数算法作了比较.     

Variational iteration method for solving twelfth-order boundary-value problems using He’s polynomials

In this paper, we apply the variational iteration method using He’s polynomials (VIMHP) for solving the twelfth-order boundary-value problems. The proposed method is an elegant combination of variational iteration and the homotopy perturbation methods. The suggested algorithm is quite efficient and is practically well suited for use in these problems. The suggested iterative scheme finds the solution without any discretization, linearization, or restrictive assumptions. Several examples are given to verify the reliability and efficiency of the method. The fact that the proposed technique solves nonlinear problems without using Adomian’s polynomials can be considered as a clear advantage of this algorithm over the decomposition method.     

A filled function method for global optimization

A novel filled function with one parameter is suggested in this paper for finding a global minimizer for a general class of nonlinear programming problems with a closed bounded box. A new algorithm is presented according to the theoretical analysis. The implementation of the algorithm on several test problems is reported with satisfactory numerical results.     

全局优化问题的一个单参数填充函数方法

利用改进的填充函数的定义,对一般的无约束最优化问题给出了一个新的单参数填充函数,分析并证明了此填充函数的性质.利用该填充函数,构造了新的算法,对此算法进行了数值实验,并将此算法做了比较,结果表明此填充函数算法是可行的.     

A New Filled Function Method for Global Optimization

A novel filled function is suggested in this paper for identifying a global minimum point for a general class of nonlinear programming problems with a closed bounded domain. Theoretical and numerical properties of the proposed filled function are investigated and a solution algorithm is proposed. The implementation of the algorithm on several test problems is reported with satisfactory numerical results.     

Computing the Invariant Polynomials of a Polynomial Matrix. I

An algorithm for computing the invariant polynomials and the canonical triangular (trapezoidal) matrix for a polynomial matrix of full column rank is suggested. The algorithm is based on the Δ W-1 rank-factorization method for solving algebraic problems for polynomial matrices, previously suggested by the author. Bibliography: 3 titles.