Nonlinear Programming Packages
This page is continuously updated!
Last modified: April 25, 2008

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.