A modified conjugate gradient method for monotone nonlinear equations with convex constraints
In this paper, a modified Hestenes-Stiefel (HS) spectral conjugate gradient (CG) method for monotone nonlinear equations with convex constraints is proposed based on projection technique. The method can be viewed as an extension of a modified HS-CG method for unconstrained optimization proposed by A...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Awwal, Aliyu Muhammed [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019transfer abstract |
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| Schlagwörter: |
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| Umfang: |
14 |
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| Übergeordnetes Werk: |
Enthalten in: Impact of rogue active regions on hemispheric asymmetry - Nagy, Melinda ELSEVIER, 2018, transactions of IMACS, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:145 ; year:2019 ; pages:507-520 ; extent:14 |
| Links: |
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| DOI / URN: |
10.1016/j.apnum.2019.05.012 |
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| Katalog-ID: |
ELV047535407 |
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| 520 | |a In this paper, a modified Hestenes-Stiefel (HS) spectral conjugate gradient (CG) method for monotone nonlinear equations with convex constraints is proposed based on projection technique. The method can be viewed as an extension of a modified HS-CG method for unconstrained optimization proposed by Amini et al. (Optimization Methods and Software, pp: 1-13, 2018). A new search direction is obtained by incorporating the idea of spectral gradient parameter and some modification of the conjugate gradient parameter. The proposed method is derivative-free and requires low memory which makes it suitable for large scale monotone nonlinear equations. Global convergence of the method is established under suitable assumptions. Preliminary numerical comparisons with some existing methods are given to show the efficiency of our proposed method. Furthermore, the proposed method is successfully applied to solve sparse signal reconstruction in compressive sensing. | ||
| 520 | |a In this paper, a modified Hestenes-Stiefel (HS) spectral conjugate gradient (CG) method for monotone nonlinear equations with convex constraints is proposed based on projection technique. The method can be viewed as an extension of a modified HS-CG method for unconstrained optimization proposed by Amini et al. (Optimization Methods and Software, pp: 1-13, 2018). A new search direction is obtained by incorporating the idea of spectral gradient parameter and some modification of the conjugate gradient parameter. The proposed method is derivative-free and requires low memory which makes it suitable for large scale monotone nonlinear equations. Global convergence of the method is established under suitable assumptions. Preliminary numerical comparisons with some existing methods are given to show the efficiency of our proposed method. Furthermore, the proposed method is successfully applied to solve sparse signal reconstruction in compressive sensing. | ||
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10.1016/j.apnum.2019.05.012 doi GBV00000000000711.pica (DE-627)ELV047535407 (ELSEVIER)S0168-9274(19)30119-9 DE-627 ger DE-627 rakwb eng 520 620 VZ 39.00 bkl 50.93 bkl Awwal, Aliyu Muhammed verfasserin aut A modified conjugate gradient method for monotone nonlinear equations with convex constraints 2019transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a modified Hestenes-Stiefel (HS) spectral conjugate gradient (CG) method for monotone nonlinear equations with convex constraints is proposed based on projection technique. The method can be viewed as an extension of a modified HS-CG method for unconstrained optimization proposed by Amini et al. (Optimization Methods and Software, pp: 1-13, 2018). A new search direction is obtained by incorporating the idea of spectral gradient parameter and some modification of the conjugate gradient parameter. The proposed method is derivative-free and requires low memory which makes it suitable for large scale monotone nonlinear equations. Global convergence of the method is established under suitable assumptions. Preliminary numerical comparisons with some existing methods are given to show the efficiency of our proposed method. Furthermore, the proposed method is successfully applied to solve sparse signal reconstruction in compressive sensing. In this paper, a modified Hestenes-Stiefel (HS) spectral conjugate gradient (CG) method for monotone nonlinear equations with convex constraints is proposed based on projection technique. The method can be viewed as an extension of a modified HS-CG method for unconstrained optimization proposed by Amini et al. (Optimization Methods and Software, pp: 1-13, 2018). A new search direction is obtained by incorporating the idea of spectral gradient parameter and some modification of the conjugate gradient parameter. The proposed method is derivative-free and requires low memory which makes it suitable for large scale monotone nonlinear equations. Global convergence of the method is established under suitable assumptions. Preliminary numerical comparisons with some existing methods are given to show the efficiency of our proposed method. Furthermore, the proposed method is successfully applied to solve sparse signal reconstruction in compressive sensing. Projection method Elsevier Nonlinear monotone equations Elsevier Compressive sensing Elsevier Spectral gradient method Elsevier Kumam, Poom oth Abubakar, Auwal Bala oth Enthalten in Elsevier Nagy, Melinda ELSEVIER Impact of rogue active regions on hemispheric asymmetry 2018 transactions of IMACS Amsterdam [u.a.] (DE-627)ELV001550608 volume:145 year:2019 pages:507-520 extent:14 https://doi.org/10.1016/j.apnum.2019.05.012 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-AST 39.00 Astronomie: Allgemeines VZ 50.93 Weltraumforschung VZ AR 145 2019 507-520 14 |
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10.1016/j.apnum.2019.05.012 doi GBV00000000000711.pica (DE-627)ELV047535407 (ELSEVIER)S0168-9274(19)30119-9 DE-627 ger DE-627 rakwb eng 520 620 VZ 39.00 bkl 50.93 bkl Awwal, Aliyu Muhammed verfasserin aut A modified conjugate gradient method for monotone nonlinear equations with convex constraints 2019transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a modified Hestenes-Stiefel (HS) spectral conjugate gradient (CG) method for monotone nonlinear equations with convex constraints is proposed based on projection technique. The method can be viewed as an extension of a modified HS-CG method for unconstrained optimization proposed by Amini et al. (Optimization Methods and Software, pp: 1-13, 2018). A new search direction is obtained by incorporating the idea of spectral gradient parameter and some modification of the conjugate gradient parameter. The proposed method is derivative-free and requires low memory which makes it suitable for large scale monotone nonlinear equations. Global convergence of the method is established under suitable assumptions. Preliminary numerical comparisons with some existing methods are given to show the efficiency of our proposed method. Furthermore, the proposed method is successfully applied to solve sparse signal reconstruction in compressive sensing. In this paper, a modified Hestenes-Stiefel (HS) spectral conjugate gradient (CG) method for monotone nonlinear equations with convex constraints is proposed based on projection technique. The method can be viewed as an extension of a modified HS-CG method for unconstrained optimization proposed by Amini et al. (Optimization Methods and Software, pp: 1-13, 2018). A new search direction is obtained by incorporating the idea of spectral gradient parameter and some modification of the conjugate gradient parameter. The proposed method is derivative-free and requires low memory which makes it suitable for large scale monotone nonlinear equations. Global convergence of the method is established under suitable assumptions. Preliminary numerical comparisons with some existing methods are given to show the efficiency of our proposed method. Furthermore, the proposed method is successfully applied to solve sparse signal reconstruction in compressive sensing. Projection method Elsevier Nonlinear monotone equations Elsevier Compressive sensing Elsevier Spectral gradient method Elsevier Kumam, Poom oth Abubakar, Auwal Bala oth Enthalten in Elsevier Nagy, Melinda ELSEVIER Impact of rogue active regions on hemispheric asymmetry 2018 transactions of IMACS Amsterdam [u.a.] (DE-627)ELV001550608 volume:145 year:2019 pages:507-520 extent:14 https://doi.org/10.1016/j.apnum.2019.05.012 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-AST 39.00 Astronomie: Allgemeines VZ 50.93 Weltraumforschung VZ AR 145 2019 507-520 14 |
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10.1016/j.apnum.2019.05.012 doi GBV00000000000711.pica (DE-627)ELV047535407 (ELSEVIER)S0168-9274(19)30119-9 DE-627 ger DE-627 rakwb eng 520 620 VZ 39.00 bkl 50.93 bkl Awwal, Aliyu Muhammed verfasserin aut A modified conjugate gradient method for monotone nonlinear equations with convex constraints 2019transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a modified Hestenes-Stiefel (HS) spectral conjugate gradient (CG) method for monotone nonlinear equations with convex constraints is proposed based on projection technique. The method can be viewed as an extension of a modified HS-CG method for unconstrained optimization proposed by Amini et al. (Optimization Methods and Software, pp: 1-13, 2018). A new search direction is obtained by incorporating the idea of spectral gradient parameter and some modification of the conjugate gradient parameter. The proposed method is derivative-free and requires low memory which makes it suitable for large scale monotone nonlinear equations. Global convergence of the method is established under suitable assumptions. Preliminary numerical comparisons with some existing methods are given to show the efficiency of our proposed method. Furthermore, the proposed method is successfully applied to solve sparse signal reconstruction in compressive sensing. In this paper, a modified Hestenes-Stiefel (HS) spectral conjugate gradient (CG) method for monotone nonlinear equations with convex constraints is proposed based on projection technique. The method can be viewed as an extension of a modified HS-CG method for unconstrained optimization proposed by Amini et al. (Optimization Methods and Software, pp: 1-13, 2018). A new search direction is obtained by incorporating the idea of spectral gradient parameter and some modification of the conjugate gradient parameter. The proposed method is derivative-free and requires low memory which makes it suitable for large scale monotone nonlinear equations. Global convergence of the method is established under suitable assumptions. Preliminary numerical comparisons with some existing methods are given to show the efficiency of our proposed method. Furthermore, the proposed method is successfully applied to solve sparse signal reconstruction in compressive sensing. Projection method Elsevier Nonlinear monotone equations Elsevier Compressive sensing Elsevier Spectral gradient method Elsevier Kumam, Poom oth Abubakar, Auwal Bala oth Enthalten in Elsevier Nagy, Melinda ELSEVIER Impact of rogue active regions on hemispheric asymmetry 2018 transactions of IMACS Amsterdam [u.a.] (DE-627)ELV001550608 volume:145 year:2019 pages:507-520 extent:14 https://doi.org/10.1016/j.apnum.2019.05.012 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-AST 39.00 Astronomie: Allgemeines VZ 50.93 Weltraumforschung VZ AR 145 2019 507-520 14 |
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10.1016/j.apnum.2019.05.012 doi GBV00000000000711.pica (DE-627)ELV047535407 (ELSEVIER)S0168-9274(19)30119-9 DE-627 ger DE-627 rakwb eng 520 620 VZ 39.00 bkl 50.93 bkl Awwal, Aliyu Muhammed verfasserin aut A modified conjugate gradient method for monotone nonlinear equations with convex constraints 2019transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a modified Hestenes-Stiefel (HS) spectral conjugate gradient (CG) method for monotone nonlinear equations with convex constraints is proposed based on projection technique. The method can be viewed as an extension of a modified HS-CG method for unconstrained optimization proposed by Amini et al. (Optimization Methods and Software, pp: 1-13, 2018). A new search direction is obtained by incorporating the idea of spectral gradient parameter and some modification of the conjugate gradient parameter. The proposed method is derivative-free and requires low memory which makes it suitable for large scale monotone nonlinear equations. Global convergence of the method is established under suitable assumptions. Preliminary numerical comparisons with some existing methods are given to show the efficiency of our proposed method. Furthermore, the proposed method is successfully applied to solve sparse signal reconstruction in compressive sensing. In this paper, a modified Hestenes-Stiefel (HS) spectral conjugate gradient (CG) method for monotone nonlinear equations with convex constraints is proposed based on projection technique. The method can be viewed as an extension of a modified HS-CG method for unconstrained optimization proposed by Amini et al. (Optimization Methods and Software, pp: 1-13, 2018). A new search direction is obtained by incorporating the idea of spectral gradient parameter and some modification of the conjugate gradient parameter. The proposed method is derivative-free and requires low memory which makes it suitable for large scale monotone nonlinear equations. Global convergence of the method is established under suitable assumptions. Preliminary numerical comparisons with some existing methods are given to show the efficiency of our proposed method. Furthermore, the proposed method is successfully applied to solve sparse signal reconstruction in compressive sensing. Projection method Elsevier Nonlinear monotone equations Elsevier Compressive sensing Elsevier Spectral gradient method Elsevier Kumam, Poom oth Abubakar, Auwal Bala oth Enthalten in Elsevier Nagy, Melinda ELSEVIER Impact of rogue active regions on hemispheric asymmetry 2018 transactions of IMACS Amsterdam [u.a.] (DE-627)ELV001550608 volume:145 year:2019 pages:507-520 extent:14 https://doi.org/10.1016/j.apnum.2019.05.012 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-AST 39.00 Astronomie: Allgemeines VZ 50.93 Weltraumforschung VZ AR 145 2019 507-520 14 |
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10.1016/j.apnum.2019.05.012 doi GBV00000000000711.pica (DE-627)ELV047535407 (ELSEVIER)S0168-9274(19)30119-9 DE-627 ger DE-627 rakwb eng 520 620 VZ 39.00 bkl 50.93 bkl Awwal, Aliyu Muhammed verfasserin aut A modified conjugate gradient method for monotone nonlinear equations with convex constraints 2019transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, a modified Hestenes-Stiefel (HS) spectral conjugate gradient (CG) method for monotone nonlinear equations with convex constraints is proposed based on projection technique. The method can be viewed as an extension of a modified HS-CG method for unconstrained optimization proposed by Amini et al. (Optimization Methods and Software, pp: 1-13, 2018). A new search direction is obtained by incorporating the idea of spectral gradient parameter and some modification of the conjugate gradient parameter. The proposed method is derivative-free and requires low memory which makes it suitable for large scale monotone nonlinear equations. Global convergence of the method is established under suitable assumptions. Preliminary numerical comparisons with some existing methods are given to show the efficiency of our proposed method. Furthermore, the proposed method is successfully applied to solve sparse signal reconstruction in compressive sensing. In this paper, a modified Hestenes-Stiefel (HS) spectral conjugate gradient (CG) method for monotone nonlinear equations with convex constraints is proposed based on projection technique. The method can be viewed as an extension of a modified HS-CG method for unconstrained optimization proposed by Amini et al. (Optimization Methods and Software, pp: 1-13, 2018). A new search direction is obtained by incorporating the idea of spectral gradient parameter and some modification of the conjugate gradient parameter. The proposed method is derivative-free and requires low memory which makes it suitable for large scale monotone nonlinear equations. Global convergence of the method is established under suitable assumptions. Preliminary numerical comparisons with some existing methods are given to show the efficiency of our proposed method. Furthermore, the proposed method is successfully applied to solve sparse signal reconstruction in compressive sensing. Projection method Elsevier Nonlinear monotone equations Elsevier Compressive sensing Elsevier Spectral gradient method Elsevier Kumam, Poom oth Abubakar, Auwal Bala oth Enthalten in Elsevier Nagy, Melinda ELSEVIER Impact of rogue active regions on hemispheric asymmetry 2018 transactions of IMACS Amsterdam [u.a.] (DE-627)ELV001550608 volume:145 year:2019 pages:507-520 extent:14 https://doi.org/10.1016/j.apnum.2019.05.012 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-AST 39.00 Astronomie: Allgemeines VZ 50.93 Weltraumforschung VZ AR 145 2019 507-520 14 |
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A modified conjugate gradient method for monotone nonlinear equations with convex constraints |
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A modified conjugate gradient method for monotone nonlinear equations with convex constraints |
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Awwal, Aliyu Muhammed |
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Impact of rogue active regions on hemispheric asymmetry |
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Impact of rogue active regions on hemispheric asymmetry |
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Awwal, Aliyu Muhammed |
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10.1016/j.apnum.2019.05.012 |
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a modified conjugate gradient method for monotone nonlinear equations with convex constraints |
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A modified conjugate gradient method for monotone nonlinear equations with convex constraints |
| abstract |
In this paper, a modified Hestenes-Stiefel (HS) spectral conjugate gradient (CG) method for monotone nonlinear equations with convex constraints is proposed based on projection technique. The method can be viewed as an extension of a modified HS-CG method for unconstrained optimization proposed by Amini et al. (Optimization Methods and Software, pp: 1-13, 2018). A new search direction is obtained by incorporating the idea of spectral gradient parameter and some modification of the conjugate gradient parameter. The proposed method is derivative-free and requires low memory which makes it suitable for large scale monotone nonlinear equations. Global convergence of the method is established under suitable assumptions. Preliminary numerical comparisons with some existing methods are given to show the efficiency of our proposed method. Furthermore, the proposed method is successfully applied to solve sparse signal reconstruction in compressive sensing. |
| abstractGer |
In this paper, a modified Hestenes-Stiefel (HS) spectral conjugate gradient (CG) method for monotone nonlinear equations with convex constraints is proposed based on projection technique. The method can be viewed as an extension of a modified HS-CG method for unconstrained optimization proposed by Amini et al. (Optimization Methods and Software, pp: 1-13, 2018). A new search direction is obtained by incorporating the idea of spectral gradient parameter and some modification of the conjugate gradient parameter. The proposed method is derivative-free and requires low memory which makes it suitable for large scale monotone nonlinear equations. Global convergence of the method is established under suitable assumptions. Preliminary numerical comparisons with some existing methods are given to show the efficiency of our proposed method. Furthermore, the proposed method is successfully applied to solve sparse signal reconstruction in compressive sensing. |
| abstract_unstemmed |
In this paper, a modified Hestenes-Stiefel (HS) spectral conjugate gradient (CG) method for monotone nonlinear equations with convex constraints is proposed based on projection technique. The method can be viewed as an extension of a modified HS-CG method for unconstrained optimization proposed by Amini et al. (Optimization Methods and Software, pp: 1-13, 2018). A new search direction is obtained by incorporating the idea of spectral gradient parameter and some modification of the conjugate gradient parameter. The proposed method is derivative-free and requires low memory which makes it suitable for large scale monotone nonlinear equations. Global convergence of the method is established under suitable assumptions. Preliminary numerical comparisons with some existing methods are given to show the efficiency of our proposed method. Furthermore, the proposed method is successfully applied to solve sparse signal reconstruction in compressive sensing. |
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A modified conjugate gradient method for monotone nonlinear equations with convex constraints |
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https://doi.org/10.1016/j.apnum.2019.05.012 |
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Kumam, Poom Abubakar, Auwal Bala |
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