半确定编程的C语言库

IF 1.4 3区数学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Optimization Methods & Software Pub Date : 1999-01-01 DOI:10.1080/10556789908805765

B. Borchers

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引用次数: 604

摘要

本文描述了CSDP，一个实现Helmberg, Rendl, Vanderbei和Wolkowicz的半确定规划算法的预测校正变体的例程库。这个代码的主要优点是，它可以作为一个独立的求解器或作为一个可调用的子程序，它是用C语言编写的，为了效率，它有效地利用了约束矩阵的稀疏性，除了线性等式约束之外，它还包括对线性不等式约束的支持。我们将讨论所使用的算法、其计算复杂度和存储需求。最后，我们给出了一系列测试问题的基准测试结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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CSDP, A C library for semidefinite programming

This paper describes CSDP, a library of routines that implements a predictor corrector variant of the semidefinite programming algorithm of Helmberg, Rendl, Vanderbei, and Wolkowicz. The main advantages of this code are that it can be used as a stand alone solver or as a callable subroutine, that it is written in C for efficiency, that it makes effective use of sparsity in the constraint matrices, and that it includes support for linear inequality constraints in addition to linear equality constraints. We discuss the algorithm used, its computational complexity, and storage requirements. Finally, we present benchmark results for a collection of test problems.

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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.