Accelerated inexact composite gradient methods for nonconvex spectral optimization problems
Abstract This paper presents two inexact composite gradient methods, one inner accelerated and another doubly accelerated, for solving a class of nonconvex spectral composite optimization problems. More specifically, the objective function for these problems is of the form %$f_{1}+f_{2}+h%$, where %...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kong, Weiwei [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
Inexact composite gradient method |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Computational optimization and applications - New York, NY [u.a.] : Springer Science + Business Media B.V., 1992, 82(2022), 3 vom: 28. Mai, Seite 673-715 |
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| Übergeordnetes Werk: |
volume:82 ; year:2022 ; number:3 ; day:28 ; month:05 ; pages:673-715 |
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| DOI / URN: |
10.1007/s10589-022-00377-9 |
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| Katalog-ID: |
SPR047344822 |
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| 245 | 1 | 0 | |a Accelerated inexact composite gradient methods for nonconvex spectral optimization problems |
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| 520 | |a Abstract This paper presents two inexact composite gradient methods, one inner accelerated and another doubly accelerated, for solving a class of nonconvex spectral composite optimization problems. More specifically, the objective function for these problems is of the form %$f_{1}+f_{2}+h%$, where %$f_{1}%$ and %$f_{2}%$ are differentiable nonconvex matrix functions with Lipschitz continuous gradients, %$h%$ is a proper closed convex matrix function, and both %$f_{2}%$ and %$h%$ can be expressed as functions that operate on the singular values of their inputs. The methods essentially use an accelerated composite gradient method to solve a sequence of proximal subproblems involving the linear approximation of %$f_{1}%$ and the singular value functions underlying %$f_{2}%$ and %$h%$. Unlike other composite gradient-based methods, the proposed methods take advantage of both the composite and spectral structure underlying the objective function in order to efficiently generate their solutions. Numerical experiments are presented to demonstrate the practicality of these methods on a set of real-world and randomly generated spectral optimization problems. | ||
| 650 | 4 | |a Composite nonconvex problem |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Iteration complexity |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Inexact composite gradient method |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a First-order accelerated gradient method |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Spectral optimization |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Monteiro, Renato D. C. |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Computational optimization and applications |d New York, NY [u.a.] : Springer Science + Business Media B.V., 1992 |g 82(2022), 3 vom: 28. Mai, Seite 673-715 |w (DE-627)266881297 |w (DE-600)1467967-X |x 1573-2894 |7 nnns |
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10.1007/s10589-022-00377-9 doi (DE-627)SPR047344822 (SPR)s10589-022-00377-9-e DE-627 ger DE-627 rakwb eng Kong, Weiwei verfasserin (orcid)0000-0002-4700-619X aut Accelerated inexact composite gradient methods for nonconvex spectral optimization problems 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract This paper presents two inexact composite gradient methods, one inner accelerated and another doubly accelerated, for solving a class of nonconvex spectral composite optimization problems. More specifically, the objective function for these problems is of the form %$f_{1}+f_{2}+h%$, where %$f_{1}%$ and %$f_{2}%$ are differentiable nonconvex matrix functions with Lipschitz continuous gradients, %$h%$ is a proper closed convex matrix function, and both %$f_{2}%$ and %$h%$ can be expressed as functions that operate on the singular values of their inputs. The methods essentially use an accelerated composite gradient method to solve a sequence of proximal subproblems involving the linear approximation of %$f_{1}%$ and the singular value functions underlying %$f_{2}%$ and %$h%$. Unlike other composite gradient-based methods, the proposed methods take advantage of both the composite and spectral structure underlying the objective function in order to efficiently generate their solutions. Numerical experiments are presented to demonstrate the practicality of these methods on a set of real-world and randomly generated spectral optimization problems. Composite nonconvex problem (dpeaa)DE-He213 Iteration complexity (dpeaa)DE-He213 Inexact composite gradient method (dpeaa)DE-He213 First-order accelerated gradient method (dpeaa)DE-He213 Spectral optimization (dpeaa)DE-He213 Monteiro, Renato D. C. aut Enthalten in Computational optimization and applications New York, NY [u.a.] : Springer Science + Business Media B.V., 1992 82(2022), 3 vom: 28. Mai, Seite 673-715 (DE-627)266881297 (DE-600)1467967-X 1573-2894 nnns volume:82 year:2022 number:3 day:28 month:05 pages:673-715 https://dx.doi.org/10.1007/s10589-022-00377-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2022 3 28 05 673-715 |
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10.1007/s10589-022-00377-9 doi (DE-627)SPR047344822 (SPR)s10589-022-00377-9-e DE-627 ger DE-627 rakwb eng Kong, Weiwei verfasserin (orcid)0000-0002-4700-619X aut Accelerated inexact composite gradient methods for nonconvex spectral optimization problems 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract This paper presents two inexact composite gradient methods, one inner accelerated and another doubly accelerated, for solving a class of nonconvex spectral composite optimization problems. More specifically, the objective function for these problems is of the form %$f_{1}+f_{2}+h%$, where %$f_{1}%$ and %$f_{2}%$ are differentiable nonconvex matrix functions with Lipschitz continuous gradients, %$h%$ is a proper closed convex matrix function, and both %$f_{2}%$ and %$h%$ can be expressed as functions that operate on the singular values of their inputs. The methods essentially use an accelerated composite gradient method to solve a sequence of proximal subproblems involving the linear approximation of %$f_{1}%$ and the singular value functions underlying %$f_{2}%$ and %$h%$. Unlike other composite gradient-based methods, the proposed methods take advantage of both the composite and spectral structure underlying the objective function in order to efficiently generate their solutions. Numerical experiments are presented to demonstrate the practicality of these methods on a set of real-world and randomly generated spectral optimization problems. Composite nonconvex problem (dpeaa)DE-He213 Iteration complexity (dpeaa)DE-He213 Inexact composite gradient method (dpeaa)DE-He213 First-order accelerated gradient method (dpeaa)DE-He213 Spectral optimization (dpeaa)DE-He213 Monteiro, Renato D. C. aut Enthalten in Computational optimization and applications New York, NY [u.a.] : Springer Science + Business Media B.V., 1992 82(2022), 3 vom: 28. Mai, Seite 673-715 (DE-627)266881297 (DE-600)1467967-X 1573-2894 nnns volume:82 year:2022 number:3 day:28 month:05 pages:673-715 https://dx.doi.org/10.1007/s10589-022-00377-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2022 3 28 05 673-715 |
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10.1007/s10589-022-00377-9 doi (DE-627)SPR047344822 (SPR)s10589-022-00377-9-e DE-627 ger DE-627 rakwb eng Kong, Weiwei verfasserin (orcid)0000-0002-4700-619X aut Accelerated inexact composite gradient methods for nonconvex spectral optimization problems 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract This paper presents two inexact composite gradient methods, one inner accelerated and another doubly accelerated, for solving a class of nonconvex spectral composite optimization problems. More specifically, the objective function for these problems is of the form %$f_{1}+f_{2}+h%$, where %$f_{1}%$ and %$f_{2}%$ are differentiable nonconvex matrix functions with Lipschitz continuous gradients, %$h%$ is a proper closed convex matrix function, and both %$f_{2}%$ and %$h%$ can be expressed as functions that operate on the singular values of their inputs. The methods essentially use an accelerated composite gradient method to solve a sequence of proximal subproblems involving the linear approximation of %$f_{1}%$ and the singular value functions underlying %$f_{2}%$ and %$h%$. Unlike other composite gradient-based methods, the proposed methods take advantage of both the composite and spectral structure underlying the objective function in order to efficiently generate their solutions. Numerical experiments are presented to demonstrate the practicality of these methods on a set of real-world and randomly generated spectral optimization problems. Composite nonconvex problem (dpeaa)DE-He213 Iteration complexity (dpeaa)DE-He213 Inexact composite gradient method (dpeaa)DE-He213 First-order accelerated gradient method (dpeaa)DE-He213 Spectral optimization (dpeaa)DE-He213 Monteiro, Renato D. C. aut Enthalten in Computational optimization and applications New York, NY [u.a.] : Springer Science + Business Media B.V., 1992 82(2022), 3 vom: 28. Mai, Seite 673-715 (DE-627)266881297 (DE-600)1467967-X 1573-2894 nnns volume:82 year:2022 number:3 day:28 month:05 pages:673-715 https://dx.doi.org/10.1007/s10589-022-00377-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2022 3 28 05 673-715 |
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10.1007/s10589-022-00377-9 doi (DE-627)SPR047344822 (SPR)s10589-022-00377-9-e DE-627 ger DE-627 rakwb eng Kong, Weiwei verfasserin (orcid)0000-0002-4700-619X aut Accelerated inexact composite gradient methods for nonconvex spectral optimization problems 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract This paper presents two inexact composite gradient methods, one inner accelerated and another doubly accelerated, for solving a class of nonconvex spectral composite optimization problems. More specifically, the objective function for these problems is of the form %$f_{1}+f_{2}+h%$, where %$f_{1}%$ and %$f_{2}%$ are differentiable nonconvex matrix functions with Lipschitz continuous gradients, %$h%$ is a proper closed convex matrix function, and both %$f_{2}%$ and %$h%$ can be expressed as functions that operate on the singular values of their inputs. The methods essentially use an accelerated composite gradient method to solve a sequence of proximal subproblems involving the linear approximation of %$f_{1}%$ and the singular value functions underlying %$f_{2}%$ and %$h%$. Unlike other composite gradient-based methods, the proposed methods take advantage of both the composite and spectral structure underlying the objective function in order to efficiently generate their solutions. Numerical experiments are presented to demonstrate the practicality of these methods on a set of real-world and randomly generated spectral optimization problems. Composite nonconvex problem (dpeaa)DE-He213 Iteration complexity (dpeaa)DE-He213 Inexact composite gradient method (dpeaa)DE-He213 First-order accelerated gradient method (dpeaa)DE-He213 Spectral optimization (dpeaa)DE-He213 Monteiro, Renato D. C. aut Enthalten in Computational optimization and applications New York, NY [u.a.] : Springer Science + Business Media B.V., 1992 82(2022), 3 vom: 28. Mai, Seite 673-715 (DE-627)266881297 (DE-600)1467967-X 1573-2894 nnns volume:82 year:2022 number:3 day:28 month:05 pages:673-715 https://dx.doi.org/10.1007/s10589-022-00377-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2022 3 28 05 673-715 |
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10.1007/s10589-022-00377-9 doi (DE-627)SPR047344822 (SPR)s10589-022-00377-9-e DE-627 ger DE-627 rakwb eng Kong, Weiwei verfasserin (orcid)0000-0002-4700-619X aut Accelerated inexact composite gradient methods for nonconvex spectral optimization problems 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract This paper presents two inexact composite gradient methods, one inner accelerated and another doubly accelerated, for solving a class of nonconvex spectral composite optimization problems. More specifically, the objective function for these problems is of the form %$f_{1}+f_{2}+h%$, where %$f_{1}%$ and %$f_{2}%$ are differentiable nonconvex matrix functions with Lipschitz continuous gradients, %$h%$ is a proper closed convex matrix function, and both %$f_{2}%$ and %$h%$ can be expressed as functions that operate on the singular values of their inputs. The methods essentially use an accelerated composite gradient method to solve a sequence of proximal subproblems involving the linear approximation of %$f_{1}%$ and the singular value functions underlying %$f_{2}%$ and %$h%$. Unlike other composite gradient-based methods, the proposed methods take advantage of both the composite and spectral structure underlying the objective function in order to efficiently generate their solutions. Numerical experiments are presented to demonstrate the practicality of these methods on a set of real-world and randomly generated spectral optimization problems. Composite nonconvex problem (dpeaa)DE-He213 Iteration complexity (dpeaa)DE-He213 Inexact composite gradient method (dpeaa)DE-He213 First-order accelerated gradient method (dpeaa)DE-He213 Spectral optimization (dpeaa)DE-He213 Monteiro, Renato D. C. aut Enthalten in Computational optimization and applications New York, NY [u.a.] : Springer Science + Business Media B.V., 1992 82(2022), 3 vom: 28. Mai, Seite 673-715 (DE-627)266881297 (DE-600)1467967-X 1573-2894 nnns volume:82 year:2022 number:3 day:28 month:05 pages:673-715 https://dx.doi.org/10.1007/s10589-022-00377-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2022 3 28 05 673-715 |
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Kong, Weiwei |
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Kong, Weiwei misc Composite nonconvex problem misc Iteration complexity misc Inexact composite gradient method misc First-order accelerated gradient method misc Spectral optimization Accelerated inexact composite gradient methods for nonconvex spectral optimization problems |
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Accelerated inexact composite gradient methods for nonconvex spectral optimization problems Composite nonconvex problem (dpeaa)DE-He213 Iteration complexity (dpeaa)DE-He213 Inexact composite gradient method (dpeaa)DE-He213 First-order accelerated gradient method (dpeaa)DE-He213 Spectral optimization (dpeaa)DE-He213 |
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accelerated inexact composite gradient methods for nonconvex spectral optimization problems |
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Accelerated inexact composite gradient methods for nonconvex spectral optimization problems |
| abstract |
Abstract This paper presents two inexact composite gradient methods, one inner accelerated and another doubly accelerated, for solving a class of nonconvex spectral composite optimization problems. More specifically, the objective function for these problems is of the form %$f_{1}+f_{2}+h%$, where %$f_{1}%$ and %$f_{2}%$ are differentiable nonconvex matrix functions with Lipschitz continuous gradients, %$h%$ is a proper closed convex matrix function, and both %$f_{2}%$ and %$h%$ can be expressed as functions that operate on the singular values of their inputs. The methods essentially use an accelerated composite gradient method to solve a sequence of proximal subproblems involving the linear approximation of %$f_{1}%$ and the singular value functions underlying %$f_{2}%$ and %$h%$. Unlike other composite gradient-based methods, the proposed methods take advantage of both the composite and spectral structure underlying the objective function in order to efficiently generate their solutions. Numerical experiments are presented to demonstrate the practicality of these methods on a set of real-world and randomly generated spectral optimization problems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstractGer |
Abstract This paper presents two inexact composite gradient methods, one inner accelerated and another doubly accelerated, for solving a class of nonconvex spectral composite optimization problems. More specifically, the objective function for these problems is of the form %$f_{1}+f_{2}+h%$, where %$f_{1}%$ and %$f_{2}%$ are differentiable nonconvex matrix functions with Lipschitz continuous gradients, %$h%$ is a proper closed convex matrix function, and both %$f_{2}%$ and %$h%$ can be expressed as functions that operate on the singular values of their inputs. The methods essentially use an accelerated composite gradient method to solve a sequence of proximal subproblems involving the linear approximation of %$f_{1}%$ and the singular value functions underlying %$f_{2}%$ and %$h%$. Unlike other composite gradient-based methods, the proposed methods take advantage of both the composite and spectral structure underlying the objective function in order to efficiently generate their solutions. Numerical experiments are presented to demonstrate the practicality of these methods on a set of real-world and randomly generated spectral optimization problems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract This paper presents two inexact composite gradient methods, one inner accelerated and another doubly accelerated, for solving a class of nonconvex spectral composite optimization problems. More specifically, the objective function for these problems is of the form %$f_{1}+f_{2}+h%$, where %$f_{1}%$ and %$f_{2}%$ are differentiable nonconvex matrix functions with Lipschitz continuous gradients, %$h%$ is a proper closed convex matrix function, and both %$f_{2}%$ and %$h%$ can be expressed as functions that operate on the singular values of their inputs. The methods essentially use an accelerated composite gradient method to solve a sequence of proximal subproblems involving the linear approximation of %$f_{1}%$ and the singular value functions underlying %$f_{2}%$ and %$h%$. Unlike other composite gradient-based methods, the proposed methods take advantage of both the composite and spectral structure underlying the objective function in order to efficiently generate their solutions. Numerical experiments are presented to demonstrate the practicality of these methods on a set of real-world and randomly generated spectral optimization problems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Accelerated inexact composite gradient methods for nonconvex spectral optimization problems |
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https://dx.doi.org/10.1007/s10589-022-00377-9 |
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Monteiro, Renato D. C. |
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10.1007/s10589-022-00377-9 |
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2024-07-04T02:48:20.518Z |
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