Reduction techniques and model analysis for linear models
Techniques for reducing the complexity of linear programs are well known. By suitable analysis many model redundancies can be removed and inconsistencies detected before an attempt is made in optimising a linear programming model. In carrying out such analysis, a structured approach is presented whereby an efficient amount of bound analysis is carried out under a row ranking scheme. In detecting new lower bounds for variables, these can be included in a starting basis by making such columns free variables. Quite often introducing new upper bounds, results in the model being more difficult to solve. We include our investigations into a strategy for deciding which new upper bounds should be passed to the optimiser. Finally most model reduction is carried out on models created by a modeling language. To aid the teaching of modeling analysis we show how such a procedure can be embedded in a modeling language and how the analysis can be presented to the modeller. We also present discussions on how the solution to a preprocessed problem, is post processed to present the solution in terms of the original problem.
- OSTI ID:
- 35763
- Report Number(s):
- CONF-9408161-; TRN: 94:009753-0018
- Resource Relation:
- Conference: 15. international symposium on mathematical programming, Ann Arbor, MI (United States), 15-19 Aug 1994; Other Information: PBD: 1994; Related Information: Is Part Of Mathematical programming: State of the art 1994; Birge, J.R.; Murty, K.G. [eds.]; PB: 312 p.
- Country of Publication:
- United States
- Language:
- English
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