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An algorithm for largescale quadratic programming
 IMA Journal of Numerical Analysis
, 1991
"... We describe a method for solving largescale general quadratic programming problems. Our method is based upon a compendium of ideas which have their origins in sparse matrix techniques and methods for solving smaller quadratic programs. We include a discussion on resolving degeneracy, on single phas ..."
Abstract

Cited by 23 (8 self)
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We describe a method for solving largescale general quadratic programming problems. Our method is based upon a compendium of ideas which have their origins in sparse matrix techniques and methods for solving smaller quadratic programs. We include a discussion on resolving degeneracy, on single phase methods and on solving parametric problems. Some numerical results are included. 1.
Some Generalizations Of The CrissCross Method For Quadratic Programming
 MATH. OPER. UND STAT. SER. OPTIMIZATION
, 1992
"... Three generalizations of the crisscross method for quadratic programming are presented here. Tucker's, Cottle's and Dantzig's principal pivoting methods are specialized as diagonal and exchange pivots for the linear complementarity problem obtained from a convex quadratic program. ..."
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Cited by 15 (9 self)
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Three generalizations of the crisscross method for quadratic programming are presented here. Tucker's, Cottle's and Dantzig's principal pivoting methods are specialized as diagonal and exchange pivots for the linear complementarity problem obtained from a convex quadratic program. A finite crisscross method, based on leastindex resolution, is constructed for solving the LCP. In proving finiteness, orthogonality properties of pivot tableaus and positive semidefiniteness of quadratic matrices are used. In the last section some special cases and two further variants of the quadratic crisscross method are discussed. If the matrix of the LCP has full rank, then a surprisingly simple algorithm follows, which coincides with Murty's `Bard type schema' in the P matrix case.