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Abstract: Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first derivatives are available, and that the constraint gradients are sparse. (Update)
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BibTeX entry: (Update)
P. E. Gill, W. Murray, and M. A. Saunders, SNOPT: An SQP algorithm for large-scale constrained optimization, Numerical Analysis Report 97-1, Department of Mathematics, University of California, San Diego, La Jolla, CA, 1997. http://citeseer.ist.psu.edu/article/gill97snopt.html More
@techreport{ gill97snopt,
author = "P. E. Gill and W. Murray and M. A. Saunders",
title = "{SNOPT}: An {SQP} algorithm for large-scale constrained optimization",
number = "{NA}--97--2",
address = "San Diego, {CA}",
year = "1997",
url = "citeseer.ist.psu.edu/article/gill97snopt.html" }
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