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Last modified: April 3, 2006

Name of the Package Author(s) Purpose
BBVSCG A.G. Buckley, A. Lenir Unconstrained optimization,
Limited memory quasi-Newton method
BTN Stephen G. Nash, George Mason University Unconstrained nonlinear minimization for parallel computers. Suitable for large-scale optimization
CG DESCENT William W. Hager, University of Florida, Department of Mathematics
Hongchao Zhang, University of Florida, Department of Mathematics
CG Papers and Software
CG+ Guanghui Liu,
Jorge Nocedal, Northwestern University
Richard Waltz, Northwestern University
CG+ is a Conjugate Gradient code for solving large-scale, unconstrained, nonlinear optimization problems.
CG+ implements three different versions of the Conjugate Gradient method: the Fletcher-Reeves method, the Polak-Ribiere method, and the positive Polak-Ribiere method (Beta always non-negative).
CONMIN David Shanno, Rutgers University
K.H. Phua, National University of Singapore
Unconstrained optimization,
Limited memory CG method
HOOKE Mark G. Johnson Unconstrained minimization,
Searching method
LBFGS Jorge Nocedal, Northwestern University,Ill. Large-scale unconstrained minimization
LBFGSB Ciyou Zhu, Richard Byrd, University of Colorado at Boulder
P.Lu-Chen, Jorge Nocedal, Northwestern University,Ill
Large-scale nonlinear optimization with simple bounds on variables
MINPACK Jorge Moré, Argonne National Laboratory
Burt Garbow, Argonne National Laboratory
Ken Hillstrom, Argonne National Laboratory
Nonlinear equations and Nonlinear least squares problems
SCALCG Neculai Andrei, Research Institute for Informatics, Bucharest 1, Romania Scaled Nonlinear Conjugate Gradient Method to find local minimizers of a differentiable function.
SCG Ernesto Birgin, Institute of Mathematics and Statistics, University of São Paulo (USP), Brazil Spectral Conjugate Gradient method to find local minimizers of a given function.
SUNDIALS Radu Serban , Center for Applied Scientific Computing
Lawrence Livermore National Laboratory
Livermore, California, USA
SUite of Nonlinear and DIfferential/ALgebraic equation Solvers consists of the following four solvers:
CVODE solves initial value problems for ordinary differential equation (ODE) systems.
CVODES solves ODE systems and includes sensitivity analysis capabilities (forward and adjoint).
IDA solves initial value problems for differential-algebraic equation (DAE) systems.
KINSOL solves nonlinear algebraic systems.
TENMIN Robert B. Schnabel , University of Colorado Unconstrained optimization
TN Stephen G. Nash, George Mason University Large-scale unconstrained minimization
TNBC Stephen G. Nash, George Mason University Large-scale nonlinear optimization with simple bounds on variables
TNPACK Tamar Schlick, Courant Institute of Mathematical Sciences
Aaron Fogelson, University of Utah
Nonlinear unconstrained minimization of large-scale separable problems
UNCMIN Robert B. Schnabel , University of Colorado Unconstrained optimization
UNO Neculai Andrei, Research Institute for Informatics, Romania Unconstrained Optimization,
Searching methods
VE08 Ph. Toint, Department of Mathematics FUNDP Namur, BELGIUM Bound constrained nonlinear optimization with an emphasis on large-scale problems