Super-fast validated solution of linear systems

Validated solution of a problem means to compute error bounds for a solution in finite precision. This includes the proof of existence of a solution. The computed error bounds are to be correct including all possible effects of rounding errors. The fastest known validation algorithm for the solution of a system of linear equations requires twice the computing time of a standard (purely) numerical algorithm. In this paper we present a super-fast validation algorithm for linear systems with symmetric positive definite matrix. This means that the entire computing time for the validation algorithm including computation of an approximated solution is the same as for a standard numerical algorithm. Numerical results are presented.     

A modified method for constructing efficient solutions structure of MOLP

This paper deals with a recently proposed algorithm for obtaining all weak efficient and efficient solutions in a multi objective linear programming (MOLP) problem. The algorithm is based on solving some weighted sum problems, and presents an easy and clear solution structure. We first present an example to show that the algorithm may fail when at least one of these weighted sum problems has not a finite optimal solution. Then, the algorithm is modified to overcome this problem. The modified algorithm determines whether an efficient solution exists for a given MOLP and generates the solution set correctly (if exists) without any change in the complexity.     

A Solution to the Weber Location Problem on the Sphere

In this paper we present an algorithm for the solution of the minisum location problem on the sphere. The algorithm is finite for the solution within a given accuracy of the optimal solution.     

Inexact smoothing method for large scale minimax optimization

A new algorithm for the solution of large scale minimax problems of a finite number of functions is introduced. The algorithm is a smoothing method based on a maximum entropy function and an inexact Newton-type algorithm for its solution. Under mild assumptions, only the approximate solution of a linear system is required at each iteration. The algorithm is shown to both globally and superlinearly convergent. Meanwhile some implementation techniques taking advantage of the sparsity of the Hessians of the functions and alleviating the disadvantage effect of the ill-conditioned matrix are considered. Numerical results show that the inexact method is considerably efficient.     

A sequential LCP method for bilevel linear programming

In this paper, we discuss an SLCP algorithm for the solution of Bilevel Linear Programs (BLP) which consists of solving a sequence of Linear Complementarity Problems (LCP) by using a hybrid enumerative method. This latter algorithm incorporates a number of procedures that reduce substantially the search for a solution of the LCP or for showing that the LCP has no solution. Computational experience with the SLCP algorithm shows that it performs quite well for the solution of small- and medium-scale BLPs with sparse structure. Furthermore, the algorithm is shown to be more efficient than a branch-and-bound method for solving the same problems.     

Finite iterative algorithms for a common solution to a group of complex matrix equations

An iterative algorithm is constructed to give a common solution to a group of complex matrix equations. By using the proposed algorithm, the existence of a common solution can be determined automatically. When a common solution exists for this group of matrix equations, it is proven by using a real inner product in complex matrix spaces as a tool that a solution can be obtained within finite iteration steps for any initial values in the absence of round-off errors. The algorithm is also generalized to solve a more general case. A numerical example is given to illustrate the effectiveness of the proposed method.     

The Discrete Linear One-sided Chebyshev Approximation

A simplex algorithm for the one-sided Chebyshev solution fromabove of overdetermined systems of linear equations is described.In this algorithm minimum storage is required, no artificialvariables are needed and no conditions are imposed on the coefficientmatrix. The described algorithm applies as well for the one-sidedChebyshev solution from below. The algorithm is a simple andfast one. Numerical results are given.     

一种求解延迟工件数最小的混合流水车间调度问题的模拟退火算法

针对延迟工件数最小的混合流水车间调度问题,给出了一种改进的模拟退火求解算法. 该算法首先给出一个启发式算法来获得初始解,然后用模拟退火算法对初始解改进. 通过交换工件在第一阶段的排序来获得一个新的解,采用最先空闲设备分配规则和先到先被加工规则,对工件在剩余各级的工序进行调度. 实验仿真表明算法是可行有效的.     

AN EFFECT ITERATION ALGORITHM FOR NUMERICAL SOLUTION OF DISCRETE HAMILTON-JACOBIBELLMAN EQUATIONS

§1Introduction ConsidertheHamilton-Jacobi-Bellmanequation max1≤v≤m[A(v)u(x)-f(v)(x)]=0,x∈Ω(1.1)withtheboundarycondition u(x)=0,x∈Ω(1.2)whereΩisabounded,smoothdomaininEuclideanspaceRd,d∈N;f(v)(x)aregiven functionsfromC2(Ω);A(v)aresecond-orderuniformlyellipticoperatorsoftheform A(v)=-d i,j=1a(v)ij2xixj+di=1b(v)ixi+c(v).(1.3)Intheaboveexpression(1.3)therearecoefficientsa(v)ij,b(v)i,c(v)∈C2(Ω)satisfying,forall1≤v≤m,a(v)ij(x)=a(v)ji(x),1≤i,j≤d,c(v)≥c0≥0,x∈Ω,a…     

一种改进的蚁群算法及其在TSP中的应用

蚁群算法是一种求解复杂组合优化问题的新的拟生态算法,也是一种基于种群的启发式仿生进化算法,属于随机搜索算法的一种,并用于较好地解决TSP问题.然而此算法也有它自己的缺陷,如易于陷入局部优化、搜索时间长等.通过对基本蚁群算法的介绍及相关因素的分析,提出了一种改进的蚁群算法,用于解决TSPLAB问题的10个问题,并与参考文献中的F-W、NCSOM、ASOM算法进行比较,计算机仿真结果表明了改进算法的有效性.如利用改进的蚁群算法解决lin105问题,其最优解为14382.995933(已知最优解为14379),相对误差是0.0209%,计算出的最小值几乎接近于已知最优解.     

Ellipsoid projection method in matrix games

A problem of calculating a solution of a zero-sum matrix game is considered in the paper The problem of search of a solution is reduced to a constrained convex minimization problem for which an ellipsoid projection algorithm is used. The algorithm generates an ?-optimal solution of the game in a polynomial time     

A nonisolated optimal solution for special reverse convex programming problems

In this paper, an efficient algorithm is proposed for globally solving special reverse convex programming problems with more than one reverse convex constraints. The proposed algorithm provides a nonisolated global optimal solution which is also stable under small perturbations of the constraints, and it turns out that such an optimal solution is adequately guaranteed to be feasible and to be close to the actual optimal solution. Convergence of the algorithm is shown and the numerical experiment is given to illustrate the feasibility of the presented algorithm.     

离散变量结构优化设计的组合算法*

本文首先给出了离散变量优化设计局部最优解的定义,然后提出了一种综合的组合算法.该算法采用分级优化的方法,第一级优化首先采用计算效率很高且经过随机抽样性能实验表明性能较高的启发式算法─—相对差商法,求解离散变量结构优化设计问题近似最优解 X ;第二级采用组合算法,在 X 的离散邻集内建立离散变量结构优化设计问题的(-1,0.1)规划模型,再进一步将其化为(0,1)规划模型,应用定界组合算法或相对差商法求解该(0,1)规划模型,求得局部最优解.解决了采用启发式算法无法判断近似最优解是否为局部最优解这一长期未得到解决的问题,提高了计算精度,同时,由于相对差商法的高效率与高精度,以上综合的组合算法的计算效率也还是较高的.     

A simulated annealing technique for multi-objective simulation optimization

In this paper, we present a simulated annealing algorithm for solving multi-objective simulation optimization problems. The algorithm is based on the idea of simulated annealing with constant temperature, and uses a rule for accepting a candidate solution that depends on the individual estimated objective function values. The algorithm is shown to converge almost surely to an optimal solution. It is applied to a multi-objective inventory problem; the numerical results show that the algorithm converges rapidly.     

Stochastic Method for the Solution of Unconstrained Vector Optimization Problems

We propose a new stochastic algorithm for the solution of unconstrained vector optimization problems, which is based on a special class of stochastic differential equations. An efficient algorithm for the numerical solution of the stochastic differential equation is developed. Interesting properties of the algorithm enable the treatment of problems with a large number of variables. Numerical results are given.     

关于 Rosen 算法的注记

1960年,Rosen 提出一个求线性不等式组可行解的投影算法.1981年,Powell给出一个例子说明 Rosen 的算法会发生循环从而失效.本文证明,按照 Rosen 的算法,只要适当地做点修正,循环就可避免,从而算法必在有限步内找到解或发现无解.首先给出一些记号.所考虑的问题是求 n 维向量 x 满足     

A Branch and Bound Method for the Multiconstraint Zero-One Knapsack Problem

This paper presents an efficient solution algorithm for the multiconstraint zero-one knapsack problem through a branch and bound search process. The algorithm has been coded in FORTRAN; and a group of thirty 5-constraint knapsack problems with 30-90 variables were run on IBM 360/75 using two other codes as well, in order to compare the computational efficiency of the proposed method with that of the original Balas and an improved Balas additive algorithms. The computational results show that the proposed method is markedly faster with regard to the total as well as the individual solution times for these test problems, and such superiority becomes more evident as the number of variables and the difficulty of the problems increase. The results also indicated that the original Balas method is extremely inefficient for the type of problems being considered here. The total solution time for the thirty problems is 13 min for the proposed method, 109 min for the improved Balas algorithm, and over 380 min for the original Balas algorithm. Extension of the solution algorithm to the generalized knapsack problem is also suggested.     

Purification for separated continuous linear programs

This paper presents a purification algorithm for a class of infinite-dimensional linear programs called separated continuous linear programs (SCLP). This takes an initial feasible solution and produces an extreme point solution without a decrease in objective function value. The algorithm presented here for SCLP is also shown to be the best possible purification algorithm in a certain class.     

Superlinear Convergence of a Newton-Type Algorithm for Monotone Equations

We consider the problem of finding solutions of systems of monotone equations. The Newton-type algorithm proposed in Ref. 1 has a very nice global convergence property in that the whole sequence of iterates generated by this algorithm converges to a solution, if it exists. Superlinear convergence of this algorithm is obtained under a standard nonsingularity assumption. The nonsingularity condition implies that the problem has a unique solution; thus, for a problem with more than one solution, such a nonsingularity condition cannot hold. In this paper, we show that the superlinear convergence of this algorithm still holds under a local error-bound assumption that is weaker than the standard nonsingularity condition. The local error-bound condition may hold even for problems with nonunique solutions. As an application, we obtain a Newton algorithm with very nice global and superlinear convergence for the minimum norm solution of linear programs.This research was supported by the Singapore-MIT Alliance and the Australian Research Council.     

An achievement rate approach to linear programming problems with an interval objective function

In this paper, we focus on a treatment of a linear programming problem with an interval objective function. From the viewpoint of the achievement rate, a new solution concept, the maximin achievement rate solution, is proposed. Nice properties of this solution are shown: a maximin achievement rate solution is necessarily optimal when a necessarily optimal solution exists, and if not, then it is still a possibly optimal solution. An algorithm for a maximin achievement rate solution is proposed based on a relaxation procedure together with a simplex method. A numerical example is given to demonstrate the proposed solution algorithm.