Nonlinear Programming Packages

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="">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.