SNOPT (Sparse Nonlinear OPTimizer) is a software package for solving large-scale optimization problems (linear and nonlinear programs). It employs a sparse SQP algorithm with limited-memory quasi-Newton approximations to the Hessian of Lagrangian. SNOPT is especially effective for nonlinear problems whose functions and gradients are expensive to evaluate. The functions should be smooth but need not be convex. An augmented Lagrangian merit function ensures convergence from an arbitrary point. Infeasible problems are treated methodically via elastic bounds on the nonlinear constraints. SNOPT allows the nonlinear constraints to be violated (if necessary) and minimizes the sum of such violations.
@techreport{snopt77,
AUTHOR = {Gill, Philip E. and Murray, Walter and Saunders, Michael A. and Wong, Elizabeth},
TITLE = {User's Guide for {SNOPT 7.7}: Software for Large-Scale Nonlinear Programming},
INSTITUTION = {Department of Mathematics, University of California, San Diego},
ADDRESS = {La Jolla, CA},
TYPE = {Center for Computational Mathematics Report},
NUMBER = {CCoM 18-1},
YEAR = 2018
}
@Article {GilMS05,
AUTHOR = {Gill, Philip E. and Murray, Walter and Saunders, Michael A.},
TITLE = {{SNOPT}: An {SQP} algorithm for large-scale constrained optimization},
JOURNAL = {SIAM Rev.},
FJOURNAL = {SIAM Review. A Publication of the Society for Industrial and
Applied Mathematics},
PAGES = {99--131},
VOLUME = {47},
YEAR = {2005}
}