A Comparative Study of Large-Scale Nonlinear Optimization Algorithms
In recent years, much work has been done on implementing a variety of algorithms in nonlinear programming software. In this paper, we analyze the performance of several state-of-the-art optimization codes on large-scale nonlinear optimization problems. Extensive numerical results are presented on different classes of problems, and features of each code that make it efficient or inefficient for each class are examined.
Keywordsinterior-point methods large-scale optimization nonlinear programming
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- H.Y. Benson, D.F. Shanno, and R.J. Vanderbei. Interior-point methods for non-convex nonlinear programming: Filter methods and merit functions. Technical Report ORFE 00–06, Department of Operations Research and Financial Engineering, Princeton University, 2000.Google Scholar
- A.R. Conn, N. Gould, and Ph.L. Toint. Constrained and unconstrained testing environment. http://www.dci.clrc.ac.uk/Activity.asp?CUTE.Google Scholar
- E. D. Dolan and J. J. Moré. Benchmarking optimization software with COPS. Technical Report ANL/MCS-246, Argonne National Laboratory, November 2000.Google Scholar
- R. Fletcher and S. Leyffer. Nonlinear programming without a penalty function. Technical Report NA/171, University of Dundee, Dept. of Mathematics, Dundee, Scotland, 1997.Google Scholar
- R. Fourer, D.M. Gay, and B.W. Kernighan. AMPL: A Modeling Language for Mathematical Programming. Scientific Press, 1993.Google Scholar
- P.E. Gill, W. Murray, and M.A. Saunders. User’s guide for SNOPT 5.3: A Fortran package for large-scale nonlinear programming. Technical report, Systems Optimization Laboratory, Stanford University, Stanford, CA, 1997.Google Scholar
- R.J. Vanderbei. AMPL models. http://orfe.princeton.edu/fvdb/ampl/nlmodels.Google Scholar
- R.J. Vanderbei. A comparison between the minimum-local-fill and minimum-degree algorithms. Technical report, AT&T Bell Laboratories, 1990.Google Scholar