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提出一种新的求解约束优化问题的遗传算法,算法通过重新定义可行解与不可行解的适应度函数分别对它们进行选择,有效避免了惩罚函数法引入参数所带来的困难,重新设计的交叉算子使得算法对解空间的寻优范围扩大了.数值实验结果表明算法具有较好的鲁棒性,且对最优解位于约束边界上的一类问题具有很大优势. 相似文献
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Recently, Roos (SIAM J Optim 16(4):1110–1136, 2006) presented a primal-dual infeasible interior-point algorithm that uses full-Newton steps and whose iteration bound coincides
with the best known bound for infeasible interior-point algorithms. In the current paper we use a different feasibility step
such that the definition of the feasibility step in Mansouri and Roos (Optim Methods Softw 22(3):519–530, 2007) is a special case of our definition, and show that the same result on the order of iteration complexity can be obtained.
相似文献
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该文对一般的凸二次规划问题,给出了一个不可行内点算法,并证明了该算法经过犗(狀2犔)步迭代之后,要么得到问题的一个近似最优解,要么说明该问题在某个较大的区域内无解. 相似文献
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This paper proposes an infeasible interior-point algorithm with full Nesterov-Todd (NT) steps for semidefinite programming (SDP). The main iteration consists of a feasibility step and several centrality steps. First we present a full NT step infeasible interior-point algorithm based on the classic logarithmical barrier function. After that a specific kernel function is introduced. The feasibility step is induced by this kernel function instead of the classic logarithmical barrier function. This kernel function has a finite value on the boundary. The result of polynomial complexity, O(nlogn/ε), coincides with the best known one for infeasible interior-point methods. 相似文献
15.
Based on a similar kernel function, we present an infeasible version of the interior-point algorithm for linear optimization introduced by Wang et al. (2016). The property of exponential convexity is still important to simplify the analysis of the algorithm. The iteration bound coincides with the currently best iteration bound for infeasible interior-point algorithms. 相似文献
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Infeasible-interior-point paths for sufficient linear complementarity problems and their analyticity
In this paper we study the behavior of infeasible-interior-point-paths for solving horizontal linear complementarity problems that are sufficient in the sense of Cottle et al. (R. W. Cottle, J.-S. Pang, Venkateswaran, Linear Algebra Appl. 114/115 (1989) 231–249). We show that these paths converge to a central point of the set of solutions. It is also shown that these are analytic functions of the path parameter even at the limitpoint, if the complementarity problem has a strictly complementary solution, and have a simple branchpoint, if it is solveable, but has no strictly complementarity solution. 相似文献
18.
In this paper an algorithm is presented for solving the classical posynomial geometric programming dual pair of problems simultaneously.
The approach is by means of a primal-dual infeasible algorithm developed simultaneously for (i) the dual geometric program
after logarithmic transformation of its objective function, and (ii) its Lagrangian dual program. Under rather general assumptions,
the mechanism defines a primal-dual infeasible path from a specially constructed, perturbed Karush-Kuhn-Tucker system.Subfeasible solutions, as described by Duffin in 1956, are generated for each program whose primal and dual objective function values
converge to the respective primal and dual program values. The basic technique is one of a predictor-corrector type involving
Newton’s method applied to the perturbed KKT system, coupled with effective techniques for choosing iterate directions and
step lengths. We also discuss implementation issues and some sparse matrix factorizations that take advantage of the very
special structure of the Hessian matrix of the logarithmically transformed dual objective function. Our computational results
on 19 of the most challenging GP problems found in the literature are encouraging. The performance indicates that the algorithm
is effective regardless of thedegree of difficulty, which is a generally accepted measure in geometric programming.
Research supported in part by the University of Iowa Obermann Fellowship and by NSF Grant DDM-9207347. 相似文献
19.
Yanjin Wang 《Numerical Functional Analysis & Optimization》2013,34(11):1283-1293
This article proposes a class of infeasible interior point algorithms for convex quadratic programming, and analyzes its complexity. It is shown that this algorithm has the polynomial complexity. Its best complexity is O(nL). 相似文献
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