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GPCG: A case study in the performance and scalability of optimization algorithms

Steve Benson (benson***at***mcs.anl.gov)
Lois McInnes (curfman***at***mcs.anl.gov)
Jorge More' (more***at***mcs.anl.gov)

Abstract: GPCG is an algorithm within the Toolkit for Advanced Optimization (TAO) for solving bound constrained, convex quadratic problems. Originally developed by More' and Toraldo, this algorithm was designed for large-scale problems but had been implemented only for a single processor. The TAO implementation is available for a wide range of high-performance architecture, and has been tested on up to 64 processors to solve problems with over 2.5 million variables.

Keywords: large-scale optimization, high-performance architectures, object-oriented design

Category 1: Optimization Software and Modeling Systems (Optimization Software Design Principles )

Category 2: Optimization Software and Modeling Systems (Parallel Algorithms )

Category 3: Optimization Software and Modeling Systems (Problem Solving Environments )

Citation: Preprint ANL/MCS-P768-0799 Mathematics and Computer Science Division Argonne National Laboratory September 2000

Download: [Compressed Postscript]

Entry Submitted: 11/17/2000
Entry Accepted: 11/20/2000
Entry Last Modified: 11/17/2000

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