|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
Limited memory CG method
|HOOKE||Mark G. Johnson||Unconstrained minimization,
|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,
|VE08||Ph. Toint, Department of Mathematics FUNDP Namur, BELGIUM||Bound constrained nonlinear optimization with an emphasis on large-scale problems|