LSGRG2

From NEOS

Nonlinear programming

LSGRG2 uses an implementation of the generalized reduced gradient (GRG) algorithm similar to that used in GRG2. However, it uses a sparse data structure to store and manipulate the constraint Jacobian matrix, and a sparse inversion procedure to factor the basis. It can therefore solve large, sparse nonlinear programs: problems with over 500 constraints have been solved successfully.

LSGRG2 requires the same user-supplied subroutines as GRG2 and has similar subroutine and data file interfaces. See the GRG2 entry for details.

LSGRG2 is written in ANSI Fortran. Machine dependencies are relegated to the subroutine INITLZ, which defines three machine-dependent constants.

LSGRG2 has been interfaced with the [gams.html GAMS] modeling language. For tests comparing LSGRG2 with MINOS on a set of large GAMS models, see the reference below.

Need more info?

Contact:

Prof. Leon Lasdon
MSIS Department
College of Business Administration
The University of Texas at Austin
Austin, TX 78712--1175
Phone: (512) 471--9433
http://www.optimalmethods.com

Reference:

S. Smith, and L. Lasdon, Solving large sparse nonlinear programs using GRG, ORSA J. Comput. 4 (1992), pp. 1--15.