
Accession Number : ADA188049
Title : A SchurComplement Method for Sparse Quadratic Programming.
Descriptive Note : Technical rept.,
Corporate Author : STANFORD UNIV CA SYSTEMS OPTIMIZATION LAB
Personal Author(s) : Gill, Philip E ; Murray, Walter ; Saunders, Michael A ; Wright, Margaret H
PDF Url : ADA188049
Report Date : Oct 1987
Pagination or Media Count : 25
Abstract : In applying activeset methods to sparse quadratic programs, it is desirable to utilize existing sparsematrix techniques. The authors describe a quadratic programming method based on the classical Schur complement. Its key feature is that much of the linear algebraic work associated wtih an entire sequence of iterations involves a fixed sparse factorization. Updates are performed at every iteration to the factorization of a smaller matrix, which may be treated as dense or sparse. The use of a fixed sparse factorization allows an offthe shelf sparse equation solver to be used repeatedly. This feature is ideally suited to problems with structure that can be exploited by a specialized factorization. Moreover, improvements in efficiency derived from exploiting new parallel and vector computer architectures are immediately applicable. An obvious application of the method is in sequential quadratic programming methods for nonlinearly constrained optimization, which require solution of a sequence of closely related quadratic programming subproblems. Some ways in which the known relationship between successive problems can be exploited are discussed. Keywords: Supercomputers; Variables; Computations.
Descriptors : *QUADRATIC PROGRAMMING, ALGEBRA, COMPUTATIONS, COMPUTER ARCHITECTURE, EQUATIONS, ITERATIONS, OFF THE SHELF EQUIPMENT, PARALLEL PROCESSORS, SEQUENCES, SEQUENTIAL ANALYSIS, SUPERCOMPUTERS, SPARSE MATRIX
Subject Categories : Operations Research
Distribution Statement : APPROVED FOR PUBLIC RELEASE