GRG2
| Author | Prof. Leon Lasdon
MSIS Department College of Business Administration The University of Texas at Austin Austin, TX 78712--1175 Phone: (512) 471-9433 Optimal Methods |

Language | FORTRAN | |

Algorithm | generalized reduced gradient (GRG) algorithm | |

Input Format | ||

Modeling Languages link | AMPL, GAMS | |

Commercial Status | A HREF="http://www.optimalmethods.com/prices.htm">Price | |

Platform | Any machine with a reasonable amount of memory and a Fortran compiler | |

Remarks | GRG2 uses an implementation of the generalized reduced gradient (GRG) algorithm.
It uses a robust implementation of the BFGS quasi-Newton algorithm as its default choice for determining a search direction. A limited-memory conjugate gradient method is also available. The problem Jacobian is stored and manipulated as a dense matrix. The GRG2 software may be used as a stand-alone system or called as a subroutine. The user is not required to supply code for first partial derivatives of problem functions; forward or central difference approximations may be used instead. Documentation includes a 60-page user's guide, in-line documentation for the subroutine interface, and complete installation instructions. | |

References | Lasdon, L.S., Fox, R.L., Ratner, M.W., (1974) Nonlinear optimization using the generalized reduced gradient method. RAIRO vol.3, Novembre 1974, pp.73-104
Lasdon, L.S., Waren, A.D., (1978) Generalized reduced gradient software for linearly and nonlinearly constrained problems. in: Greenberg, H.J., (Ed.) Design and Implementation of Optimization Software. Sijthoff and Noordhoff, Holland, 1978, pp.335-362.
L. S. Lasdon, A. D. Warren, A. Jain, and M. Ratner, Design and testing of a generalized reduced gradient code for nonlinear programming, ACM Trans. Math. Software 4 (1978), pp. 34--50. |