This report concerns with the solution of linearly constrained strictly convex quadratic programming problem by a splitting iterative method, called diagonalization algorithm (DA). This algorithm transforms the original problem into a sequence of subproblems easier to solve, for which there exists a large number of efficient methods in literature. In fact each subproblem may be formulated as a linear complementarity problem or as a constrained least distance problem. We give conditions for DA convergence and we present an application on a large scale sparse problem arising in constrained bivariate interpolation. An extensive experimentation on CRAY C94 permits to evaluate the DA performance.The Fortran 77 codes carried out on multivector computer Cray C94 implementing the algorithms described above, are reported in appendix.

Galligani, Emanuele, Ruggiero, V. e Luca, Zanni. "The diagonalization method for strictly convex quadratic programs: analysis and implementationon vector computers" Working paper, CNR, Collana del progetto finalizzato “Sistemi Informatici e Calcolo Parallelo”, sottoprogetto 1 "Calcolo Scientifico per Grandi Sistemi", 1995.

I documenti presenti in Iris Unimore sono rilasciati con licenza Creative Commons Attribuzione - Non commerciale - Non opere derivate 3.0 Italia, salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Powered by IRIS-about IRIS-Utilizzo dei cookie

 Copyright © 2021