Column generation in quadratic programming

Beer, Klaus; Käschel, Joachim

doi:10.1080/02331937908842561

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…For procedures of these special types, we refer exemplarily to Wolfe (1976), Beer and K~ischel (1979), Murty and Fathi (1982), Fujishige and Zhan (1990), A1-Sultan and Murty (1992), Sekitani and Yamamoto (1993). The method of Schwartz and K~ischel (1981) determines a hyperplane separating {0} from a convex polytope not containing zero and can be used if one is only interested in a lower bound for…”

Section: I~5 a J~[k]mentioning

confidence: 99%

Error bounds for solutions of linear equations and inequalities

Klatte

Thiere²

1995

ZOR - Mathematical Methods of Operations Research

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Given a system of linear equations and inequalities in n variables, a famous result due to A. J. Hoffman (1952) says that the distance of any point in R" to the solution set of this system is bounded above by the product of a positive constant and the absolute residual. We shall discuss explicit representations of this constant in dependence upon the pair of norms used for the estimation. A method for computing a special form of Hoffman constants is proposed. Finally, we use these results in the analysis of Lipsehitz continuity for solutions of parametric quadratic programs.

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Section: I~5 a J~[k]mentioning

confidence: 99%

Error bounds for solutions of linear equations and inequalities

Klatte

Thiere²

1995

ZOR - Mathematical Methods of Operations Research

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“…RMP is solved using continuous relaxation, which at the end requires integer rounding of the results. There has been some work for application of column generation technique in quadratic programming [19][20][21].…”

Section: Ps ℓ;E Torsmentioning

confidence: 99%