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dc.contributor.authorBrown, Gerald G.
dc.contributor.authorOlson, Michael
dc.date1994
dc.date.accessioned2014-01-09T22:21:12Z
dc.date.available2014-01-09T22:21:12Z
dc.date.issued1994
dc.identifier.citationBrown, G.G. and Olson, M., 1994, “Dynamic Factorization in Large-Scale Optimization,” Mathematical Programming, 64, pp. 17-51.
dc.identifier.urihttp://hdl.handle.net/10945/38133
dc.descriptionMathematical Programming, 64, pp. 17-51.en_US
dc.description.abstractFactorization of linear programming (LP) models enables a large portion of the LP tableau to be represented implicitly and generated from the remaining explicit part. Dynamic factorization admits algebraic elements which change in dimension during the course of solution. A unifying mathematical framework for dynamic row factorization is presented with three algorithms which derive from different LP model row structures: generalized upper bound rows, pure network rows,and generalized network TOWS. Each of these structures is a generalization of its predecessors, and each corresponding algorithm exhibits just enough additional richness to accommodate the structure at hand within the unified framework. Implementation and computational results are presented for a variety of real-world models. These results suggest that each of these algorithms is superior to the traditional, non-factorized approach, with the degree of improvement depending upon the size and quality of the row factorization identified.en_US
dc.rightsdefined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleDynamic Factorization in Large-Scale Optimizationen_US
dc.typeArticleen_US
dc.contributor.departmentOperations Research (OR)
dc.subject.authorFactorizationen_US
dc.subject.authorLinear programmingen_US
dc.subject.authorGeneralized upper boundsen_US
dc.subject.authorPure networksen_US
dc.subject.authorGeneralized networksen_US


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