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Monotone projected gradient methods for large-scale box-constrained quadratic programming

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Abstract

Inspired by the success of the projected Barzilai-Borwein (PBB) method for large-scale box-constrained quadratic programming, we propose and analyze the monotone projected gradient methods in this paper. We show by experiments and analyses that for the new methods, it is generally a bad option to compute steplengths based on the negative gradients. Thus in our algorithms, some continuous or discontinuous projected gradients are used instead to compute the steplengths. Numerical experiments on a wide variety of test problems are presented, indicating that the new methods usually outperform the PBB method.

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Correspondence to Gao Li.

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Zhou, B., Gao, L. & Dai, Y. Monotone projected gradient methods for large-scale box-constrained quadratic programming. SCI CHINA SER A 49, 688–702 (2006). https://doi.org/10.1007/s11425-006-0688-2

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  • DOI: https://doi.org/10.1007/s11425-006-0688-2

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