Improved Predictor-Corrector Algorithm

Hassan Pazira

doi:10.1007/978-3-030-34585-3_9

Improved Predictor-Corrector Algorithm

Hassan Pazira^*

^*Corresponding author for this work

Epidemiology and Data Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

The differential geometric least angle regression method consists essentially in computing the solution path. In Augugliaro et al. [4], this problem is satisfactorily solved by using a predictor-corrector (PC) algorithm, that however has the drawback of becoming intractable when working with thousands of predictors. Using the PC algorithm leads to an increase in the run times needed for computing the solution curve. In this paper we explain an improved version of the PC algorithm (IPC), proposed in Pazira et al. [9], to decrease the effects stemming from this problem for computing the solution curve. The IPC algorithm allows the dgLARS method to be implemented by using less number of arithmetic operations that leads to potential computational saving.

Original language	English
Title of host publication	Computational Intelligence Methods for Bioinformatics and Biostatistics - 15th International Meeting, CIBB 2018, Revised Selected Papers
Editors	Maria Raposo, Susana Sério, Antonino Staiano, Angelo Ciaramella
Publisher	Springer
Pages	99-106
Volume	11925 LNBI
ISBN (Print)	9783030345846
DOIs	https://doi.org/10.1007/978-3-030-34585-3_9
Publication status	Published - 2020
Event	15th International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2018 - Caparica, Puerto Rico Duration: 6 Sept 2018 → 8 Sept 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2018
Country/Territory	Puerto Rico
City	Caparica
Period	06/09/2018 → 08/09/2018

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10.1007/978-3-030-34585-3_9

Cite this

Pazira, H. (2020). Improved Predictor-Corrector Algorithm. In M. Raposo, S. Sério, A. Staiano, & A. Ciaramella (Eds.), Computational Intelligence Methods for Bioinformatics and Biostatistics - 15th International Meeting, CIBB 2018, Revised Selected Papers (Vol. 11925 LNBI, pp. 99-106). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Springer. https://doi.org/10.1007/978-3-030-34585-3_9

Pazira, Hassan. / Improved Predictor-Corrector Algorithm. Computational Intelligence Methods for Bioinformatics and Biostatistics - 15th International Meeting, CIBB 2018, Revised Selected Papers. editor / Maria Raposo ; Susana Sério ; Antonino Staiano ; Angelo Ciaramella. Vol. 11925 LNBI Springer, 2020. pp. 99-106 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Improved Predictor-Corrector Algorithm",

abstract = "The differential geometric least angle regression method consists essentially in computing the solution path. In Augugliaro et al. [4], this problem is satisfactorily solved by using a predictor-corrector (PC) algorithm, that however has the drawback of becoming intractable when working with thousands of predictors. Using the PC algorithm leads to an increase in the run times needed for computing the solution curve. In this paper we explain an improved version of the PC algorithm (IPC), proposed in Pazira et al. [9], to decrease the effects stemming from this problem for computing the solution curve. The IPC algorithm allows the dgLARS method to be implemented by using less number of arithmetic operations that leads to potential computational saving.",

author = "Hassan Pazira",

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doi = "10.1007/978-3-030-34585-3_9",

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booktitle = "Computational Intelligence Methods for Bioinformatics and Biostatistics - 15th International Meeting, CIBB 2018, Revised Selected Papers",

note = "15th International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2018 ; Conference date: 06-09-2018 Through 08-09-2018",

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Pazira, H 2020, Improved Predictor-Corrector Algorithm. in M Raposo, S Sério, A Staiano & A Ciaramella (eds), Computational Intelligence Methods for Bioinformatics and Biostatistics - 15th International Meeting, CIBB 2018, Revised Selected Papers. vol. 11925 LNBI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, pp. 99-106, 15th International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2018, Caparica, Puerto Rico, 06/09/2018. https://doi.org/10.1007/978-3-030-34585-3_9

Improved Predictor-Corrector Algorithm. / Pazira, Hassan.

Computational Intelligence Methods for Bioinformatics and Biostatistics - 15th International Meeting, CIBB 2018, Revised Selected Papers. ed. / Maria Raposo; Susana Sério; Antonino Staiano; Angelo Ciaramella. Vol. 11925 LNBI Springer, 2020. p. 99-106 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AB - The differential geometric least angle regression method consists essentially in computing the solution path. In Augugliaro et al. [4], this problem is satisfactorily solved by using a predictor-corrector (PC) algorithm, that however has the drawback of becoming intractable when working with thousands of predictors. Using the PC algorithm leads to an increase in the run times needed for computing the solution curve. In this paper we explain an improved version of the PC algorithm (IPC), proposed in Pazira et al. [9], to decrease the effects stemming from this problem for computing the solution curve. The IPC algorithm allows the dgLARS method to be implemented by using less number of arithmetic operations that leads to potential computational saving.

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Pazira H. Improved Predictor-Corrector Algorithm. In Raposo M, Sério S, Staiano A, Ciaramella A, editors, Computational Intelligence Methods for Bioinformatics and Biostatistics - 15th International Meeting, CIBB 2018, Revised Selected Papers. Vol. 11925 LNBI. Springer. 2020. p. 99-106. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-34585-3_9

Improved Predictor-Corrector Algorithm

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