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141.
Grundel D. A. Oliveira C. A. S. Pardalos P. M. 《Journal of Optimization Theory and Applications》2004,122(3):487-500
The multidimensional assignment problem (MAP) is a NP-hard combinatorial optimization problem, occurring in many applications, such as data association. In this paper, we prove two conjectures made in Ref. 1 and based on data from computational experiments on MAPs. We show that the mean optimal objective function cost of random instances of the MAP goes to zero as the problem size increases, when assignment costs are independent exponentially or uniformly distributed random variables. We prove also that the mean optimal solution goes to negative infinity when assignment costs are independent normally distributed random variables. 相似文献
142.
Currently, most combinatorial optimisation problems have to be solved, if the optimum solution is sought, using general techniques
to explore the space of feasible solutions and, more specifically, through exploratory enumerative procedures in trees and
search graphs. We propose Branch and Win, a general formulation for understanding and synthesising the different tree search
procedures that have been presented in the literature of operations research as well as in that of artificial intelligence.
Several general ideas are also presented, whose application allows designing new hybrid search algorithms, in order to implement
the procedure. 相似文献
143.
We develop two implementable algorithms, the first for the solution of finite and the second for the solution of semi-infinite min-max-min problems. A smoothing technique (together with discretization for the semi-infinite case) is used to construct a sequence of approximating finite min-max problems, which are solved with increasing precision. The smoothing and discretization approximations are initially coarse, but are made progressively finer as the number of iterations is increased. This reduces the potential ill-conditioning due to high smoothing precision parameter values and computational cost due to high levels of discretization. The behavior of the algorithms is illustrated with three semi-infinite numerical examples. 相似文献
144.
Logic-Based Modeling and Solution of Nonlinear Discrete/Continuous Optimization Problems 总被引:2,自引:0,他引:2
This paper presents a review of advances in the mathematical programming approach to discrete/continuous optimization problems.
We first present a brief review of MILP and MINLP for the case when these problems are modeled with algebraic equations and
inequalities. Since algebraic representations have some limitations such as difficulty of formulation and numerical singularities
for the nonlinear case, we consider logic-based modeling as an alternative approach, particularly Generalized Disjunctive
Programming (GDP), which the authors have extensively investigated over the last few years. Solution strategies for GDP models
are reviewed, including the continuous relaxation of the disjunctive constraints. Also, we briefly review a hybrid model that
integrates disjunctive programming and mixed-integer programming. Finally, the global optimization of nonconvex GDP problems
is discussed through a two-level branch and bound procedure. 相似文献
145.
In this paper, we continue the analysis of the image regularity condition (IRC) as introduced in a previous paper where we have proved that IRC implies the existence of generalized Lagrange-John multipliers with first component equal to 1. The term generalized is connected with the fact that the separation (in the image space) is not necessarily linear (when we have classic Lagrange-John multipliers), but it can be also nonlinear. Here, we prove that the IRC guarantees, also in the nondifferentiable case, the fact that 0 is a solution of the first-order homogeneized (linearized) problem obtained by means of the Dini-Hadamard derivatives. 相似文献
146.
Finding Robust Solutions Using Local Search 总被引:1,自引:0,他引:1
Kenneth Sörensen 《Journal of Mathematical Modelling and Algorithms》2004,3(1):89-103
This paper investigates how a local search metaheuristic for continuous optimisation can be adapted so that it finds broad
peaks, corresponding to robust solutions. This is relevant in problems in which uncertain or noisy data is present. When using
a genetic or evolutionary algorithm, it is standard practice to perturb solutions once before evaluating them, using noise
from a given distribution. This approach however, is not valid when using population-less techniques like local search and
other heuristics that use local search. For those algorithms to find robust solutions, each solution needs to be perturbed
and evaluated several times, and these evaluations need to be combined into a measure of robustness. In this paper, we examine
how many of these evaluations are needed to reliably find a robust solution. We also examine the effect of the parameters
of the noise distribution. Using a simple tabu search procedure, the proposed approach is tested on several functions found
in the literature.
This revised version was published online in August 2006 with corrections to the Cover Date. 相似文献
147.
In this paper, we use an algebraic type of closure, which is called vector closure, and through it we introduce some adaptations to the proper efficiency in the sense of Hurwicz, Benson, and Borwein in real linear spaces without any particular topology. Scalarization, multiplier rules, and saddle-point theorems are obtained in order to characterize the proper efficiency in vector optimization with and without constraints. The usual convexlikeness concepts used in such theorems are weakened through the vector closure. 相似文献
148.
吴慧卓 《纯粹数学与应用数学》2003,19(1):16-21
典型的进化策略受自然进化过程的启发而成为求解全局优化问题的重要方法。传统的ES变异算子作为一个主要的进化技术是建立在正态分布的随机变量基础上的,本文提出了基于指数分布的进化策略由于采用了新的变异算子有效地减少了产生探试解的成本,从而优于传统的进化策略。 相似文献
149.
针对带约束的凸多面体线性不确定模型,提出了一种新型鲁棒预测控制方法,它采用离散化的不确定模型构造最小-最大优化控制问题,并在其中直接引入状态反馈机制,与其他最小-最大预测控制方法相比,这种方法等效于增加了控制序列的长度,为优化问题增加了更多的自由度,从而扩大了可行域,作为最小化目标的是离散化不确定系统在整个预测时域上二次型成本函数的最大值,而不是各预测阶段应成本项的上界之和,从而减少了与最小-最大优化相关的方程个数,有利于降低计算复杂性,文中进一步证明了不确定系统的闭环稳定性取决于优化问题在初始时刻的可行性,并将优化问题转化为线性矩阵不等式形式。最后,以数值仿赵例子验证了方法的有效性。 相似文献
150.
In this paper we present a new method for the solution of optimization problems with PDE constraints. It is based on simultaneous pseudo-time stepping for evolution equations. The new method can be viewed as a continuous reduced SQP method in the sense that it uses a preconditioner derived from that method. The reduced Hessian in the preconditioner is approximated by a pseudo-differential operator, whose symbol can be investigated analytically. We apply our method to a boundary control model problem. The new optimization method needs 3.2-times the overall computational effort of the solution of simulation problem alone. 相似文献