Performance analysis of algorithms for Large-Scale Nonlinear Optimization

摘要

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 algorithm's stability is very necessary.