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GIPALS - Linear Programming Environment 1.2.1

Publisher: Optimalon Software
Filesize: 988 KB
License: Shareware
Price: $295.00
Updated: 2006-10-08


GIPALS is linear programming environment that incorporates large-scale linear programs solver and easy, intuitive graphical user interface to direct specify or import and solve any type of constrained optimization problems arising in various industrial, financial and educational areas.

Constrained optimization problems are stated as linear programs with UNLIMITED number of decision variables and constraints.

The linear program solver is based on Interior-Point method (Mehrotra predictor-corrector algorithm) and optimized for large sparse linear programs by implementing the state-of-art algorithm to order the constraints matrix.

The user can specify the linear program constraints in a dense form using the grids or in a sparse form using the particular constraints editor. The dense form is suitable for small and medium linear programs with non-zeros prevail over zeros. In this form the constraints can be directly copied/pasted from/to MS Excel spreadsheets by Windows clipboard. The sparse form is designed to specify / view / edit large linear programs with tens and hundreds of thousand variables and constraints.

GIPALS can import linear programs from Mathematical Programming System (MPS) data format that is an industry standard for the description of a variety of linear programs. Any linear program specified in GIPALS' user interface could be exported to MPS format.

The solution of the linear programs can be saved as CSV (comma-delimited spreadsheet), Tab-delimited or HTML file.

Key features of GIPALS:

Simple and natural way to specify a linear program without any special mathematical knowledge;

Robust Interior-Point method for fast and reliable solution;

Support the industrial standard format of linear programs;

Report the solutions in widely used formats including spreadsheets and HTML.

See also: linear programming, linear program, constrained optimization, optimization, large-scale, matrix, sparse matrix, primal dual, interior point, predictor-corrector, constraint, MPS, GIPALS, LP


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