NDLI: Performance analysis of algorithms for large-scale nonlinear optimization

Content Provider	IEEE Xplore Digital Library
Author	Aipeng Jiang Jingtao Huang Zhoushu Jiang Jian Wang Guohui Huang Qiang Ding
Copyright Year	2008
Description	Author affiliation: Sch. of Autom., Hangzhou Dianzi Univ., Hangzhou (Aipeng Jiang)
Abstract	With the development of optimization heads toward large-scale problems, a series of optimization packages were developed for large-scale NLP optimization. The RSQP (reduced sequential quadratic programming) we concerned is one of best large scale NLP algorithm, and is especially efficient in process operation optimization. In this paper, the performance of our concerned algorithm RSQP, CONOPT2 and MINOS5 were tested by large-scale benchmarking COPS examples, the calculation was under the environment of GAMS. Computing results demonstrate that the performance of CONOPT2 was better than the others on robustness and efficiency. To problems with relatively small degrees of freedom, the performance of MINOS5 worked worst, though RSQP was not as robust as CONOPT2, its memory required reduced as half as CONOPT2. The results show that the RSQP we concerned has great advances for large scale optimization and further research for the algorithmpsilas stability is very necessary.
Starting Page	7333
Ending Page	7338
File Size	172423
Page Count	6
File Format	PDF
ISBN	9781424421138
DOI	10.1109/WCICA.2008.4594581
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Optimization Methanol Quadratic programming Algorithm design and analysis Automation Artificial neural networks Educational institutions CONOPT large-scale optimization COPS examples degrees of freedom RSQP MINOS
Content Type	Text
Resource Type	Article

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