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Data and performance of an active-set truncated Newton method with non-monotone line search for bound-constrained optimization.

ABSTRACT: In this data article, we report data and experiments related to the research article entitled "A Two-Stage Active-Set Algorithm for Bound-Constrained Optimization", by Cristofari et al. (2017). The method proposed in Cristofari et al. (2017), tackles optimization problems with bound constraints by properly combining an active-set estimate with a truncated Newton strategy. Here, we report the detailed numerical experience performed over a commonly used test set, namely CUTEst (Gould et al., 2015). First, the algorithm ASA-BCP proposed in Cristofari et al. (2017) is compared with the related method NMBC (De Santis et al., 2012). Then, a comparison with the renowned methods ALGENCAN (Birgin and Martínez et al., 2002) and LANCELOT B (Gould et al., 2003) is reported.

SUBMITTER: Cristofari A

PROVIDER: S-EPMC6265501 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Data and performance of an active-set truncated Newton method with non-monotone line search for bound-constrained optimization.

Cristofari A A De Santis M M Lucidi S S Rinaldi F F

Data in brief 20181120

In this data article, we report data and experiments related to the research article entitled "A Two-Stage Active-Set Algorithm for Bound-Constrained Optimization", by Cristofari et al. (2017). The method proposed in Cristofari et al. (2017), tackles optimization problems with bound constraints by properly combining an active-set estimate with a truncated Newton strategy. Here, we report the detailed numerical experience performed over a commonly used test set, namely CUTEst (Gould et al., 2015) ...[more]

PMID: 30533467

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Dataset Information

Data and performance of an active-set truncated Newton method with non-monotone line search for bound-constrained optimization.

Publications

Data and performance of an active-set truncated Newton method with non-monotone line search for bound-constrained optimization.

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