GPCG: A Case Study in the Performance and Scalability of Optimization Algorithms
<|reference_start|>GPCG: A Case Study in the Performance and Scalability of Optimization Algorithms: 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.<|reference_end|>
@article{benson2001gpcg:,
    title={GPCG: A Case Study in the Performance and Scalability of Optimization
  Algorithms},
    author={Steven J. Benson, Lois Curfman McInnes, and Jorge J. Mor'e},
    journal={arXiv preprint arXiv:cs/0101018},
    year={2001},
    number={ANL/MCS-P768-0799},
    archivePrefix={arXiv},
    eprint={cs/0101018},
    primaryClass={cs.MS}
}