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Name of the Package	Author(s)	Purpose
APPSPACK	Tamara G. Kolda, Sandia National Labs, tgkolda@sandia.gov Patricia D. Hough, Sandia National Labs, pdhough@sandia.gov Genetha Gray, Sandia National Labs, gagray@sandia.gov Josh Griffin, Sandia National Labs, jgriffi@sandia.gov R. Michael Lewis, College of William & Mary (cddlib interface) Robert Darwin (Sandia Summer Intern, 2004) Daniel Dunlavy (Sandia Summer Intern, 2001) H. Alton Patrick (Sandia Summer Intern, 2000) Sarah Brown (Sandia Summer Intern, 2000)	APPSPACK is serial or parallel, derivative-free optimization software for solving nonlinear unconstrained, bound-constrained, and linearly-constrained optimization problems, with possibly noisy and expensive objective functions.
CONOPT	Arne S. Drud, ARKI Consulting & Development, Denmark	Nonlinear Programming with Sparse Nonlinear Constraints.
COPL_LC	Yinyu Ye, Iowa University	Linearly Constrained Optimization
DONLP2	Peter Spellucci, Technical University Darmstadt, Germany	Minimization of smooth nonlinear functions subject to smooth constraints
EA3	J.G. Ecker, Rensselaer Polytechnic Institute M. Kupferschmid, Rensselaer Polytechnic Institute	Nonlinear Programming, Ellipsoid method
FSQP	Eliane R. Panier, University of Maryland Andre Tits, University of Maryland Jian Zhou, Craig Lawrence	Multiple competing linear/nonlinear objective functions (minimax) with: - linear/nonlinear inequality constraints. - linear/nonlinear equality constraints.
GRG2	Leon Lasdon, The University of Texas at Austin	Nonlinear Programming
IPOPT	Andreas Waechter, IBM T. J. Watson Research Center P.O. Box 218 Yorktown Heights, NY 10598 Lorenz T. Biegler, Yi-Dong Lang, Arvind Raghunathan Department of Chemical Engineering Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213, USA.	Large-Scale Nonlinear Optimization. IPOPT
KNITRO	Richard Byrd, University of Colorado Jorge Nocedal, Northwestern University, Evanston, Illinois Richard Waltz, Northwestern University, Evanston, Illinois Mary Beth Hribar Guanghui Lui with assistance from: Todd Plantenga, Sandia National Laboratories Marcelo Marazzi,Northwestern University, Evanston, Illinois	Nonlinear programming
LANCELOT	Andy Conn, IBM T.J. Watson Research Center, NY, USA Nick Gould, Rutherford Appleton Laboratory, UK Philippe Toint, Facultés Universitaires Notre Dame de la Paix, Belgium	Large-scale Optimization
LSGRG2	Leon Lasdon, The University of Texas at Austin	Large-scale Nonlinear Programming
LSSOL	Philip Gill, University of California, San Diego Walter Murray, Stanford University Michael A. Saunders, Stanford University Margaret H. Wright, AT&T Bell Laboratories	Dense linear and quadratic programs (convex), and constrained linear least-squares problems.
MINOPT	C. Schweiger, Princeton University Christodoulos A. Floudas, Princeton University	Linear, Mixed-Integer, Nonlinear, Dynamic, and Mixed-Integer Nonlinear Optimization
MINOS	Bruce A. Murtagh, University of New South Wales, Australia Michael A. Saunders, Stanford University	Large-scale linear and nonlinear programs
MINQ	Arnold Neumaier, Universität Wien, Austria	Bound Constrained Indefinite Quadratic Programming
NITRO	Richard Byrd, University of Colorado, Boulder Mary Hribar, Rice University Jorge Nocedal, Northwestern University, Evanston	Large-scale Nonlinear Programming
NLPQL	K. Schittkowski, University of Bayreuth, Germany	Nonlinear Optimization
NLPQLB	K. Schittkowski, University of Bayreuth, Germany	Smooth nonlinear programming with very many constraints
NLPSPR	John T. Betts, Boeing Computer Services Paul D. Frank, Boeing Computer Services	Nonlinear Programming
NPSOL	Philip Gill, University of California, San Diego Walter Murray, Stanford University Michael A. Saunders, Stanford University Margaret H. Wright, AT&T Bell Laboratories	Dense linear and nonlinear programs.
OPTIMA Library	M. C. Bartholomew-Biggs, University of Hertfordshire, United Kingdom	Unconstrained optimization, constrained optimization, sensitivity analysis
OPTPACK	William W. Hager, University of Florida	Unconstrained optimization and nonlinear constrained optimization
QPOPT	Philip Gill, University of California, San Diego Walter Murray, Stanford University Michael A. Saunders, Stanford University	Dense linear and quadratic programs (non-convex).
SNOPT	Philip Gill, University of California, San Diego Walter Murray, Stanford University Michael A. Saunders, Stanford University	Large-scale linear and nonlinear programs.
SolvOpt	Alexei V. Kuntsevich, Karl-Franzens Universität Graz, Austria Franz Kappel	Nonlinear Optimization, possibly non-smooth
SPENBAR	Neculai Andrei, Research Institute for Informatics, Romania	Nonlinear Programming
SQOPT	Philip Gill, University of California, San Diego Walter Murray, Stanford University Michael A. Saunders, Stanford University	Large-scale linear and quadratic programs.
TANGO	J. M. Martínez, UNICAMP, Brazil E. G. Birgin, University of São Paulo, Brazil	Trustable Algorithms for Nonlinear General Optimization
TOLMIN	M.J.D. Powell, Cambridge University, England	Linearly Constrained Optimization
TRON	Chih-Jen Lin,National Taiwan University. Jorge Moré, Argonne National Laboratory.	Large bound-constrained optimization problems.
PENOPT	Michal Kocvara Michael Stingl PENOPT GbR Georg-Geyer-Ring 5 95643 Tirschenreuth, Germany phone: +49-9631-798688 fax: +49-9631-798689 email: contact@penopt.com	* nonlinear programming: PENNLP for general (smooth) large-scale nonlinear optimization and one of the fastest codes for (smooth) convex optimization. * linear semidefinite programming: PENSDP solves optimization problems with linear matrix inequality constraints. It is one of the most efficient codes available for large-scale sparse problems. * bilinear matrix inequalities: PENBMI is the first available code that (locally) solves optimization problems with bilinear matrix inequality constraints.