具有Monteiro-Zhang统一搜索方向的半定规划长步预测校正内点算法

IF 1.4 3区数学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Optimization Methods & Software Pub Date : 1999-01-01 DOI:10.1080/10556789908805747

M. Shida

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引用次数: 0

摘要

利用Monteiro-Zhang统一搜索方向，提出了一种求解单调半定线性互补问题的长步预测校正内点算法。我们的算法基于Kojima、Shida和Shindoh提出的使用alizadeh - haebery - overton搜索方向的长步预测校正内点算法，尽管ho搜索方向一般不属于MZ统一搜索方向。

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On long-step predictor-corrector interior-point algorithm for semidefinite programming with Monteiro-Zhang unified search directions

We present a long-step predictor-corrector interior-point algorithm for the monotone semidefinite linear complementarity problems using the Monteiro-Zhang unified search directions. Our algorithm is based on the long-step predictor-corrector interior-point algorithm proposed by Kojima, Shida and Shindoh using the Alizadeh-Haeberly-Overton search direction, though the AHO search direction does not belong to the MZ unified search directions in general.

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来源期刊

Optimization Methods & Software 工程技术-计算机：软件工程

CiteScore

4.50

自引率

0.00%

发文量

审稿时长

7 months

期刊介绍： Optimization Methods and Software publishes refereed papers on the latest developments in the theory and realization of optimization methods, with particular emphasis on the interface between software development and algorithm design. Topics include: Theory, implementation and performance evaluation of algorithms and computer codes for linear, nonlinear, discrete, stochastic optimization and optimal control. This includes in particular conic, semi-definite, mixed integer, network, non-smooth, multi-objective and global optimization by deterministic or nondeterministic algorithms. Algorithms and software for complementarity, variational inequalities and equilibrium problems, and also for solving inverse problems, systems of nonlinear equations and the numerical study of parameter dependent operators. Various aspects of efficient and user-friendly implementations: e.g. automatic differentiation, massively parallel optimization, distributed computing, on-line algorithms, error sensitivity and validity analysis, problem scaling, stopping criteria and symbolic numeric interfaces. Theoretical studies with clear potential for applications and successful applications of specially adapted optimization methods and software to fields like engineering, machine learning, data mining, economics, finance, biology, or medicine. These submissions should not consist solely of the straightforward use of standard optimization techniques.