http://www.cnr.it/ontology/cnr/individuo/prodotto/ID83494
Randomized combinatorial algorithms for linear programming when the dimension is moderately high (Contributo in atti di convegno)
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- Label
- Randomized combinatorial algorithms for linear programming when the dimension is moderately high (Contributo in atti di convegno) (literal)
- Anno
- 2003-01-01T00:00:00+01:00 (literal)
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- In the last decade researchers in computational geometry have produced
a series of algorithms for linear programming, based on a randomized
combinatorial approach, which are tuned for linear programs where the number of
variables d is small compared to the number n of constraints, although not so small
to be considered a constant of the problem. One natural question is how practical
are these algorithms for classes of LP instances not necessarily derived from
problems in computational geometry. In this paper, building within the randomized
combinatorial
approach, we propose two algorithms for linear programming and we give
evidence of their empirical running times on several classes of randomly generated
instances. Comparisons with state of the art free software (lp solve) and state of the
art commercial software (Cplex) lead to the conclusion that the randomized
combinatorial approach for systems with n \approx d, at the present state of our
research, can be competitive for dense systems and for sparse systems where a
large fraction of the constraints are equalities. We also consider the case of dense
systems where n >> d, which is typical in instances from computational geometry
problems, for which we improve upon recent results of Gartner and Schonherr.
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- Titolo
- Randomized combinatorial algorithms for linear programming when the dimension is moderately high (literal)
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