On three-term conjugate gradient method for optimization problems with applications on COVID-19 model and robotic motion control
Abstract The three-term conjugate gradient (CG) algorithms are among the efficient variants of CG algorithms for solving optimization models. This is due to their simplicity and low memory requirements. On the other hand, the regression model is one of the statistical relationship models whose solut...
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| Autor*in: |
Sulaiman, Ibrahim Mohammed [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Advances in difference equations - [S.l.] : Springer International, 2004, 2022(2022), 1 vom: 04. Jan. |
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| Übergeordnetes Werk: |
volume:2022 ; year:2022 ; number:1 ; day:04 ; month:01 |
| Links: |
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| DOI / URN: |
10.1186/s13662-021-03638-9 |
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| 520 | |a Abstract The three-term conjugate gradient (CG) algorithms are among the efficient variants of CG algorithms for solving optimization models. This is due to their simplicity and low memory requirements. On the other hand, the regression model is one of the statistical relationship models whose solution is obtained using one of the least square methods including the CG-like method. In this paper, we present a modification of a three-term conjugate gradient method for unconstrained optimization models and further establish the global convergence under inexact line search. The proposed method was extended to formulate a regression model for the novel coronavirus (COVID-19). The study considers the globally infected cases from January to October 2020 in parameterizing the model. Preliminary results have shown that the proposed method is promising and produces efficient regression model for COVID-19 pandemic. Also, the method was extended to solve a motion control problem involving a two-joint planar robot. | ||
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| 650 | 4 | |a Coronavirus (COVID-19) |7 (dpeaa)DE-He213 | |
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10.1186/s13662-021-03638-9 doi (DE-627)SPR045884218 (SPR)s13662-021-03638-9-e DE-627 ger DE-627 rakwb eng Sulaiman, Ibrahim Mohammed verfasserin (orcid)0000-0001-5246-6636 aut On three-term conjugate gradient method for optimization problems with applications on COVID-19 model and robotic motion control 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract The three-term conjugate gradient (CG) algorithms are among the efficient variants of CG algorithms for solving optimization models. This is due to their simplicity and low memory requirements. On the other hand, the regression model is one of the statistical relationship models whose solution is obtained using one of the least square methods including the CG-like method. In this paper, we present a modification of a three-term conjugate gradient method for unconstrained optimization models and further establish the global convergence under inexact line search. The proposed method was extended to formulate a regression model for the novel coronavirus (COVID-19). The study considers the globally infected cases from January to October 2020 in parameterizing the model. Preliminary results have shown that the proposed method is promising and produces efficient regression model for COVID-19 pandemic. Also, the method was extended to solve a motion control problem involving a two-joint planar robot. Finite difference (dpeaa)DE-He213 Three-term CG algorithms (dpeaa)DE-He213 Optimization models (dpeaa)DE-He213 Motion control (dpeaa)DE-He213 Line search procedure (dpeaa)DE-He213 Coronavirus (COVID-19) (dpeaa)DE-He213 Regression analysis (dpeaa)DE-He213 Malik, Maulana (orcid)0000-0003-3060-0624 aut Awwal, Aliyu Muhammed (orcid)0000-0002-1040-3626 aut Kumam, Poom (orcid)0000-0002-5463-4581 aut Mamat, Mustafa (orcid)0000-0002-4802-3733 aut Al-Ahmad, Shadi (orcid)0000-0001-7690-1444 aut Enthalten in Advances in difference equations [S.l.] : Springer International, 2004 2022(2022), 1 vom: 04. Jan. (DE-627)377755699 (DE-600)2132815-8 1687-1847 nnns volume:2022 year:2022 number:1 day:04 month:01 https://dx.doi.org/10.1186/s13662-021-03638-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_206 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4305 AR 2022 2022 1 04 01 |
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10.1186/s13662-021-03638-9 doi (DE-627)SPR045884218 (SPR)s13662-021-03638-9-e DE-627 ger DE-627 rakwb eng Sulaiman, Ibrahim Mohammed verfasserin (orcid)0000-0001-5246-6636 aut On three-term conjugate gradient method for optimization problems with applications on COVID-19 model and robotic motion control 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract The three-term conjugate gradient (CG) algorithms are among the efficient variants of CG algorithms for solving optimization models. This is due to their simplicity and low memory requirements. On the other hand, the regression model is one of the statistical relationship models whose solution is obtained using one of the least square methods including the CG-like method. In this paper, we present a modification of a three-term conjugate gradient method for unconstrained optimization models and further establish the global convergence under inexact line search. The proposed method was extended to formulate a regression model for the novel coronavirus (COVID-19). The study considers the globally infected cases from January to October 2020 in parameterizing the model. Preliminary results have shown that the proposed method is promising and produces efficient regression model for COVID-19 pandemic. Also, the method was extended to solve a motion control problem involving a two-joint planar robot. Finite difference (dpeaa)DE-He213 Three-term CG algorithms (dpeaa)DE-He213 Optimization models (dpeaa)DE-He213 Motion control (dpeaa)DE-He213 Line search procedure (dpeaa)DE-He213 Coronavirus (COVID-19) (dpeaa)DE-He213 Regression analysis (dpeaa)DE-He213 Malik, Maulana (orcid)0000-0003-3060-0624 aut Awwal, Aliyu Muhammed (orcid)0000-0002-1040-3626 aut Kumam, Poom (orcid)0000-0002-5463-4581 aut Mamat, Mustafa (orcid)0000-0002-4802-3733 aut Al-Ahmad, Shadi (orcid)0000-0001-7690-1444 aut Enthalten in Advances in difference equations [S.l.] : Springer International, 2004 2022(2022), 1 vom: 04. Jan. (DE-627)377755699 (DE-600)2132815-8 1687-1847 nnns volume:2022 year:2022 number:1 day:04 month:01 https://dx.doi.org/10.1186/s13662-021-03638-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_206 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4305 AR 2022 2022 1 04 01 |
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10.1186/s13662-021-03638-9 doi (DE-627)SPR045884218 (SPR)s13662-021-03638-9-e DE-627 ger DE-627 rakwb eng Sulaiman, Ibrahim Mohammed verfasserin (orcid)0000-0001-5246-6636 aut On three-term conjugate gradient method for optimization problems with applications on COVID-19 model and robotic motion control 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract The three-term conjugate gradient (CG) algorithms are among the efficient variants of CG algorithms for solving optimization models. This is due to their simplicity and low memory requirements. On the other hand, the regression model is one of the statistical relationship models whose solution is obtained using one of the least square methods including the CG-like method. In this paper, we present a modification of a three-term conjugate gradient method for unconstrained optimization models and further establish the global convergence under inexact line search. The proposed method was extended to formulate a regression model for the novel coronavirus (COVID-19). The study considers the globally infected cases from January to October 2020 in parameterizing the model. Preliminary results have shown that the proposed method is promising and produces efficient regression model for COVID-19 pandemic. Also, the method was extended to solve a motion control problem involving a two-joint planar robot. Finite difference (dpeaa)DE-He213 Three-term CG algorithms (dpeaa)DE-He213 Optimization models (dpeaa)DE-He213 Motion control (dpeaa)DE-He213 Line search procedure (dpeaa)DE-He213 Coronavirus (COVID-19) (dpeaa)DE-He213 Regression analysis (dpeaa)DE-He213 Malik, Maulana (orcid)0000-0003-3060-0624 aut Awwal, Aliyu Muhammed (orcid)0000-0002-1040-3626 aut Kumam, Poom (orcid)0000-0002-5463-4581 aut Mamat, Mustafa (orcid)0000-0002-4802-3733 aut Al-Ahmad, Shadi (orcid)0000-0001-7690-1444 aut Enthalten in Advances in difference equations [S.l.] : Springer International, 2004 2022(2022), 1 vom: 04. Jan. (DE-627)377755699 (DE-600)2132815-8 1687-1847 nnns volume:2022 year:2022 number:1 day:04 month:01 https://dx.doi.org/10.1186/s13662-021-03638-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_206 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4305 AR 2022 2022 1 04 01 |
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10.1186/s13662-021-03638-9 doi (DE-627)SPR045884218 (SPR)s13662-021-03638-9-e DE-627 ger DE-627 rakwb eng Sulaiman, Ibrahim Mohammed verfasserin (orcid)0000-0001-5246-6636 aut On three-term conjugate gradient method for optimization problems with applications on COVID-19 model and robotic motion control 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract The three-term conjugate gradient (CG) algorithms are among the efficient variants of CG algorithms for solving optimization models. This is due to their simplicity and low memory requirements. On the other hand, the regression model is one of the statistical relationship models whose solution is obtained using one of the least square methods including the CG-like method. In this paper, we present a modification of a three-term conjugate gradient method for unconstrained optimization models and further establish the global convergence under inexact line search. The proposed method was extended to formulate a regression model for the novel coronavirus (COVID-19). The study considers the globally infected cases from January to October 2020 in parameterizing the model. Preliminary results have shown that the proposed method is promising and produces efficient regression model for COVID-19 pandemic. Also, the method was extended to solve a motion control problem involving a two-joint planar robot. Finite difference (dpeaa)DE-He213 Three-term CG algorithms (dpeaa)DE-He213 Optimization models (dpeaa)DE-He213 Motion control (dpeaa)DE-He213 Line search procedure (dpeaa)DE-He213 Coronavirus (COVID-19) (dpeaa)DE-He213 Regression analysis (dpeaa)DE-He213 Malik, Maulana (orcid)0000-0003-3060-0624 aut Awwal, Aliyu Muhammed (orcid)0000-0002-1040-3626 aut Kumam, Poom (orcid)0000-0002-5463-4581 aut Mamat, Mustafa (orcid)0000-0002-4802-3733 aut Al-Ahmad, Shadi (orcid)0000-0001-7690-1444 aut Enthalten in Advances in difference equations [S.l.] : Springer International, 2004 2022(2022), 1 vom: 04. Jan. (DE-627)377755699 (DE-600)2132815-8 1687-1847 nnns volume:2022 year:2022 number:1 day:04 month:01 https://dx.doi.org/10.1186/s13662-021-03638-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_206 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4305 AR 2022 2022 1 04 01 |
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10.1186/s13662-021-03638-9 doi (DE-627)SPR045884218 (SPR)s13662-021-03638-9-e DE-627 ger DE-627 rakwb eng Sulaiman, Ibrahim Mohammed verfasserin (orcid)0000-0001-5246-6636 aut On three-term conjugate gradient method for optimization problems with applications on COVID-19 model and robotic motion control 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract The three-term conjugate gradient (CG) algorithms are among the efficient variants of CG algorithms for solving optimization models. This is due to their simplicity and low memory requirements. On the other hand, the regression model is one of the statistical relationship models whose solution is obtained using one of the least square methods including the CG-like method. In this paper, we present a modification of a three-term conjugate gradient method for unconstrained optimization models and further establish the global convergence under inexact line search. The proposed method was extended to formulate a regression model for the novel coronavirus (COVID-19). The study considers the globally infected cases from January to October 2020 in parameterizing the model. Preliminary results have shown that the proposed method is promising and produces efficient regression model for COVID-19 pandemic. Also, the method was extended to solve a motion control problem involving a two-joint planar robot. Finite difference (dpeaa)DE-He213 Three-term CG algorithms (dpeaa)DE-He213 Optimization models (dpeaa)DE-He213 Motion control (dpeaa)DE-He213 Line search procedure (dpeaa)DE-He213 Coronavirus (COVID-19) (dpeaa)DE-He213 Regression analysis (dpeaa)DE-He213 Malik, Maulana (orcid)0000-0003-3060-0624 aut Awwal, Aliyu Muhammed (orcid)0000-0002-1040-3626 aut Kumam, Poom (orcid)0000-0002-5463-4581 aut Mamat, Mustafa (orcid)0000-0002-4802-3733 aut Al-Ahmad, Shadi (orcid)0000-0001-7690-1444 aut Enthalten in Advances in difference equations [S.l.] : Springer International, 2004 2022(2022), 1 vom: 04. Jan. (DE-627)377755699 (DE-600)2132815-8 1687-1847 nnns volume:2022 year:2022 number:1 day:04 month:01 https://dx.doi.org/10.1186/s13662-021-03638-9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_206 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4305 AR 2022 2022 1 04 01 |
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Sulaiman, Ibrahim Mohammed misc Finite difference misc Three-term CG algorithms misc Optimization models misc Motion control misc Line search procedure misc Coronavirus (COVID-19) misc Regression analysis On three-term conjugate gradient method for optimization problems with applications on COVID-19 model and robotic motion control |
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On three-term conjugate gradient method for optimization problems with applications on COVID-19 model and robotic motion control Finite difference (dpeaa)DE-He213 Three-term CG algorithms (dpeaa)DE-He213 Optimization models (dpeaa)DE-He213 Motion control (dpeaa)DE-He213 Line search procedure (dpeaa)DE-He213 Coronavirus (COVID-19) (dpeaa)DE-He213 Regression analysis (dpeaa)DE-He213 |
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On three-term conjugate gradient method for optimization problems with applications on COVID-19 model and robotic motion control |
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Abstract The three-term conjugate gradient (CG) algorithms are among the efficient variants of CG algorithms for solving optimization models. This is due to their simplicity and low memory requirements. On the other hand, the regression model is one of the statistical relationship models whose solution is obtained using one of the least square methods including the CG-like method. In this paper, we present a modification of a three-term conjugate gradient method for unconstrained optimization models and further establish the global convergence under inexact line search. The proposed method was extended to formulate a regression model for the novel coronavirus (COVID-19). The study considers the globally infected cases from January to October 2020 in parameterizing the model. Preliminary results have shown that the proposed method is promising and produces efficient regression model for COVID-19 pandemic. Also, the method was extended to solve a motion control problem involving a two-joint planar robot. © The Author(s) 2021 |
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Abstract The three-term conjugate gradient (CG) algorithms are among the efficient variants of CG algorithms for solving optimization models. This is due to their simplicity and low memory requirements. On the other hand, the regression model is one of the statistical relationship models whose solution is obtained using one of the least square methods including the CG-like method. In this paper, we present a modification of a three-term conjugate gradient method for unconstrained optimization models and further establish the global convergence under inexact line search. The proposed method was extended to formulate a regression model for the novel coronavirus (COVID-19). The study considers the globally infected cases from January to October 2020 in parameterizing the model. Preliminary results have shown that the proposed method is promising and produces efficient regression model for COVID-19 pandemic. Also, the method was extended to solve a motion control problem involving a two-joint planar robot. © The Author(s) 2021 |
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Abstract The three-term conjugate gradient (CG) algorithms are among the efficient variants of CG algorithms for solving optimization models. This is due to their simplicity and low memory requirements. On the other hand, the regression model is one of the statistical relationship models whose solution is obtained using one of the least square methods including the CG-like method. In this paper, we present a modification of a three-term conjugate gradient method for unconstrained optimization models and further establish the global convergence under inexact line search. The proposed method was extended to formulate a regression model for the novel coronavirus (COVID-19). The study considers the globally infected cases from January to October 2020 in parameterizing the model. Preliminary results have shown that the proposed method is promising and produces efficient regression model for COVID-19 pandemic. Also, the method was extended to solve a motion control problem involving a two-joint planar robot. © The Author(s) 2021 |
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