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1.
The solution of quadratic programming problems is an important issue in the field of mathematical programming and industrial
applications. In this paper, we solve convex quadratic programming by a potential-reduction interior—point algorithm. It is
proved that the potential—reduction interior-point algorithm is globally convergent. Some numerical experiments were made. 相似文献
2.
Mehrotra's recent suggestion of a predictor-corrector variant of primal-dual interior-point method for linear programming is currently the interior-point method of choice for linear programming. In this work the authors give a predictor-corrector interior-point algorithm for monotone variational inequality problems. The algorithm was proved to be equivalent to a level-1 perturbed composite Newton method. Computations in the algorithm do not require the initial iteration to be feasible. Numerical results of experiments are presented. 相似文献
3.
In this paper, a new primal-dual interior-point algorithm for convex quadratic optimization (CQO) based on a kernel function is presented. The proposed function has some properties that are easy for checking. These properties enable us to improve the polynomial complexity bound of a large-update interior-point method (IPM) to O (√nlognlogn/ε), which is the currently best known polynomial complexity bound for the algorithm with the large-update method. Numerical tests were conducted to investigate the behavior of the algorithm with different parameters p, q and θ, where p is the growth degree parameter, q is the barrier degree of the kernel function and θ is the barrier update parameter. 相似文献
4.
针对凸二次规划问题,构造了新的核函数.通过构造的核函数来确定搜索方向和逼近度量,接着给出了求解凸二次规划问题的全牛顿步内点算法,最后给出了算法的复杂性界. 相似文献
5.
The choice of self-concordant functions is the key to efficient algorithms for linear and quadratic convex optimizations,
which provide a method with polynomial-time iterations to solve linear and quadratic convex optimization problems. The parameters
of a self-concordant barrier function can be used to compute the complexity bound of the proposed algorithm. In this paper,
it is proved that the finite barrier function is a local self-concordant barrier function. By deriving the local values of
parameters of this barrier function, the desired complexity bound of an interior-point algorithm based on this local self-concordant
function for linear optimization problem is obtained. The bound matches the best known bound for small-update methods.
Project supported by the National Natural Science Foundation of China (Grant No.10771133), the Shanghai Leading Academic Discipline
Project (Grant No.S30101), and the Research Foundation for the Doctoral Program of Higher Education (Grant No.200802800010) 相似文献
6.
用于线性优化的基于核函数的动态步长原-对偶内点算法 总被引：1，自引：2，他引：1
In this paper, primal-dual interior-point algorithm with dynamic step size is implemented for linear programming （LP） problems. The algorithms are based on a few kernel functions, including both serf-regular functions and non-serf-regular ones. The dynamic step size is compared with fixed step size for the algorithms in inner iteration of Newton step. Numerical tests show that the algorithms with dynaraic step size are more efficient than those with fixed step size. 相似文献
7.
马圣容 《南京晓庄学院学报》2011,27(3):19-22
目前已经有许多关于凸二次规划问题的研究,如文[1][2][5]等,文章对文[1]所给的原始-对偶内点算法理论上的某些缺陷加以更正,给出了框式约束凸二次规划问题的一个修正原始-对偶内点算法并进行了证明. 相似文献
8.
提出了一种修正的SQP算法求解带约束的极大极小问题,仅添加一个额外的变量,将带约束的极大极小问题转化为序列二次规划问题。证明了在合理的假设条件下,序列二次规划问题的极小值点就是原问题的极小值点。数值结果表明这种SQP算法是求解带约束有限极大极小问题的一种有效算法。 相似文献
9.
针对线性约束非凸二次规划问题,从其KKT点出发得到它的一个线性松弛规划,并递归地向该松弛规划中加入原问题的互补松弛条件的线性等式,从而得到一个有限分支定界算法,并对其收敛性进行了证明,经数值实验表明该算法是有效的. 相似文献
10.
In this paper, primal-dual interior-point algorithm with dynamic step size is implemented for linear programming (LP) problems.
The algorithms are based on a few kernel functions, including both self-regular functions and non-self-regular ones. The dynamic
step size is compared with fixed step size for the algorithms in inner iteration of Newton step. Numerical tests show that
the algorithms with dynamic step size are more efficient than those with fixed step size.
Project supported by Dutch Organization for Scientific Research (Grant No.613.000.010) 相似文献
11.
刘海林 《赣南师范学院学报》1998,(3)
文［１］提了了一个修正的Ｌｕｅｎｂｅｒｇｅｒ二次规划的秩一算法并分析了它的收敛（有限步终止）问题,本文进一步分析有限步终结问题,而且得到当二次函数的特征值在某个数一侧时,则算法至多ｎ步终止 相似文献
12.
Interior-point methods （IPMs） for linear optimization （LO） and semidefinite optimization （SDO） have become a hot area in mathematical programming in the last decades. In this paper, a new kernel function with simple algebraic expression is proposed. Based on this kernel function, a primal-dual interior-point methods （IPMs） for semidefinite optimization （SDO） is designed. And the iteration complexity of the algorithm as O（n^3/4 log n/ε） with large-updates is established. The resulting bound is better than the classical kernel function, with its iteration complexity O（n log n/ε） in large-updates case. 相似文献
13.
在可行方向算法的基础之上,加入了精确的一维搜索(牛顿法),对二次规划问题提出了一种可行方向算法,并以实例说明此算法是很有效的。 相似文献
14.
刘海林 《广东技术师范学院学报》1997,(4)
本文提出了一个无约束二次规划的秩一算法，该算法对Davidon算法进行了改进并赋以一维搜索，证明了迭代矩阵的正定性，从而搜索方向是下降方向。此外得到了该算法有限步收敛的结果。 相似文献
15.
文章主要讨论了严格凸二次规划的求解,结合Cholesky分解思想,对严格凸二次规划问题进行了预处理,并且通过数值试验对预处理前后的二次规划的求解进行了比较,数值实验取得了较好的效果 相似文献
16.
In this paper, a new branch-and-bound algorithm based on the Lagrangian dual relaxation and continuous relaxation is proposed for discrete multi-factor portfolio selection model with roundlot restriction in financial optimization. This discrete portfolio model is of integer quadratic programming problems. The separable structure of the model is investigated by using Lagrangian relaxation and dual search. Computational results show that the algorithm is capable of solving real-world portfolio problems with data from US stock market and randomly generated test problems with up to 120 securities. 相似文献
17.
针对多目标无约束0—1二次规划问题,提出一种文化基因算法。该算法采用基于分解的多目标演化算法框架,能够获得分布均匀的非占优解;同时,采用一种简单、有效的禁忌搜索,能够利用更多问题相关的信息,获得质量更优的非占优解。该算法在优化的过程中能够动态地平衡多样性与收敛性。实验结果证明该算法能够很好地求解多目标无约束0-1二次规划问题,并且性能优于目前求解该问题较先进的算法。 相似文献
18.
A mechanism for proving global convergence in filter-SQP(sequence of quadratic programming)method with the nonlinear complementarity problem(NCP)function is described for constrained nonlinear optimization problem.We introduce an NCP function into the filter and construct a new SQP-filter algorithm.Such methods are characterized by their use of the dominance concept of multi-objective optimization,instead of a penalty parameter whose adjustment can be problematic.We prove that the algorithm has global convergence and superlinear convergence rates under some mild conditions. 相似文献
19.
为了改进求解大规模约束条件的半定规划问题的方法.首先通过经典的二次正则法,将一般的半定规划问题(SDP)的标准形式进行形式的转化.然后通过研究转化后问题的最优性条件,给出了求解一般的半定规划问题的正则化算法及其收敛性证明.在实际中,处理大规模约束条件的半定规划问题(SDP)时,该方法表现出很好的性能. 相似文献
20.