CSDP 2.3 user's guide

IF 1.4 3区数学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Optimization Methods & Software Pub Date : 1999-01-01 DOI:10.1080/10556789908805764

B. Borchers

引用次数: 51

Abstract

The CSDP software package consists of a subroutine library for solving semidefinite programming problems, a stand alone solver for solving problems in the SDPA sparse format, some examples showing how to use CSDP, and utility programs for converting between SDPA sparse problem format and the SDPpack problem format. This user's guide describes how to install and use the software.

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CSDP 2.3用户指南

CSDP软件包包括用于求解半定规划问题的子程序库、用于求解SDPA稀疏格式问题的独立求解器、说明如何使用CSDP的一些示例以及用于在SDPA稀疏问题格式和SDPpack问题格式之间进行转换的实用程序。本用户指南介绍如何安装和使用该软件。

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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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.