Fast proximal algorithms for nonsmooth convex optimization
In the lines of our previous approach to devise proximal algorithms for nonsmooth convex optimization by applying Nesterov fast gradient concept to the Moreau–Yosida regularization of a convex function, we develop three new proximal algorithms for nonsmooth convex optimization. In these algorithms,...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ouorou, Adam [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2020 |
|---|
| Schlagwörter: |
|---|
| Umfang: |
7 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Impact of Lung Flute Therapy on Asthma: A Pilot Study - Parikh, Neil U. ELSEVIER, 2017, a journal of INFORMS devoted to the rapid publication of concise contributions in operations research, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:48 ; year:2020 ; number:6 ; pages:777-783 ; extent:7 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.orl.2020.09.008 |
|---|
| Katalog-ID: |
ELV052004511 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV052004511 | ||
| 003 | DE-627 | ||
| 005 | 20230624181832.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 210910s2020 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.orl.2020.09.008 |2 doi | |
| 028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001201.pica |
| 035 | |a (DE-627)ELV052004511 | ||
| 035 | |a (ELSEVIER)S0167-6377(20)30146-2 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 610 |q VZ |
| 082 | 0 | 4 | |a 610 |q VZ |
| 084 | |a 44.85 |2 bkl | ||
| 100 | 1 | |a Ouorou, Adam |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Fast proximal algorithms for nonsmooth convex optimization |
| 264 | 1 | |c 2020 | |
| 300 | |a 7 | ||
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a In the lines of our previous approach to devise proximal algorithms for nonsmooth convex optimization by applying Nesterov fast gradient concept to the Moreau–Yosida regularization of a convex function, we develop three new proximal algorithms for nonsmooth convex optimization. In these algorithms, the errors in computing approximate solutions for the Moreau–Yosida regularization are not fixed beforehand, while preserving the complexity estimates already established. We report some preliminary computational results to give a first estimate of their performance. | ||
| 650 | 7 | |a Nesterov accelerated gradient method |2 Elsevier | |
| 650 | 7 | |a Proximal methods |2 Elsevier | |
| 650 | 7 | |a Nonsmooth optimization |2 Elsevier | |
| 650 | 7 | |a Convex programming |2 Elsevier | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Parikh, Neil U. ELSEVIER |t Impact of Lung Flute Therapy on Asthma: A Pilot Study |d 2017 |d a journal of INFORMS devoted to the rapid publication of concise contributions in operations research |g Amsterdam [u.a.] |w (DE-627)ELV014727307 |
| 773 | 1 | 8 | |g volume:48 |g year:2020 |g number:6 |g pages:777-783 |g extent:7 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.orl.2020.09.008 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a SSG-OLC-PHA | ||
| 912 | |a GBV_ILN_40 | ||
| 936 | b | k | |a 44.85 |j Kardiologie |j Angiologie |q VZ |
| 951 | |a AR | ||
| 952 | |d 48 |j 2020 |e 6 |h 777-783 |g 7 | ||
| author_variant |
a o ao |
|---|---|
| matchkey_str |
ouorouadam:2020----:atrxmlloihsonnmohov |
| hierarchy_sort_str |
2020 |
| bklnumber |
44.85 |
| publishDate |
2020 |
| allfields |
10.1016/j.orl.2020.09.008 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001201.pica (DE-627)ELV052004511 (ELSEVIER)S0167-6377(20)30146-2 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 44.85 bkl Ouorou, Adam verfasserin aut Fast proximal algorithms for nonsmooth convex optimization 2020 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In the lines of our previous approach to devise proximal algorithms for nonsmooth convex optimization by applying Nesterov fast gradient concept to the Moreau–Yosida regularization of a convex function, we develop three new proximal algorithms for nonsmooth convex optimization. In these algorithms, the errors in computing approximate solutions for the Moreau–Yosida regularization are not fixed beforehand, while preserving the complexity estimates already established. We report some preliminary computational results to give a first estimate of their performance. Nesterov accelerated gradient method Elsevier Proximal methods Elsevier Nonsmooth optimization Elsevier Convex programming Elsevier Enthalten in Elsevier Science Parikh, Neil U. ELSEVIER Impact of Lung Flute Therapy on Asthma: A Pilot Study 2017 a journal of INFORMS devoted to the rapid publication of concise contributions in operations research Amsterdam [u.a.] (DE-627)ELV014727307 volume:48 year:2020 number:6 pages:777-783 extent:7 https://doi.org/10.1016/j.orl.2020.09.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 44.85 Kardiologie Angiologie VZ AR 48 2020 6 777-783 7 |
| spelling |
10.1016/j.orl.2020.09.008 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001201.pica (DE-627)ELV052004511 (ELSEVIER)S0167-6377(20)30146-2 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 44.85 bkl Ouorou, Adam verfasserin aut Fast proximal algorithms for nonsmooth convex optimization 2020 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In the lines of our previous approach to devise proximal algorithms for nonsmooth convex optimization by applying Nesterov fast gradient concept to the Moreau–Yosida regularization of a convex function, we develop three new proximal algorithms for nonsmooth convex optimization. In these algorithms, the errors in computing approximate solutions for the Moreau–Yosida regularization are not fixed beforehand, while preserving the complexity estimates already established. We report some preliminary computational results to give a first estimate of their performance. Nesterov accelerated gradient method Elsevier Proximal methods Elsevier Nonsmooth optimization Elsevier Convex programming Elsevier Enthalten in Elsevier Science Parikh, Neil U. ELSEVIER Impact of Lung Flute Therapy on Asthma: A Pilot Study 2017 a journal of INFORMS devoted to the rapid publication of concise contributions in operations research Amsterdam [u.a.] (DE-627)ELV014727307 volume:48 year:2020 number:6 pages:777-783 extent:7 https://doi.org/10.1016/j.orl.2020.09.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 44.85 Kardiologie Angiologie VZ AR 48 2020 6 777-783 7 |
| allfields_unstemmed |
10.1016/j.orl.2020.09.008 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001201.pica (DE-627)ELV052004511 (ELSEVIER)S0167-6377(20)30146-2 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 44.85 bkl Ouorou, Adam verfasserin aut Fast proximal algorithms for nonsmooth convex optimization 2020 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In the lines of our previous approach to devise proximal algorithms for nonsmooth convex optimization by applying Nesterov fast gradient concept to the Moreau–Yosida regularization of a convex function, we develop three new proximal algorithms for nonsmooth convex optimization. In these algorithms, the errors in computing approximate solutions for the Moreau–Yosida regularization are not fixed beforehand, while preserving the complexity estimates already established. We report some preliminary computational results to give a first estimate of their performance. Nesterov accelerated gradient method Elsevier Proximal methods Elsevier Nonsmooth optimization Elsevier Convex programming Elsevier Enthalten in Elsevier Science Parikh, Neil U. ELSEVIER Impact of Lung Flute Therapy on Asthma: A Pilot Study 2017 a journal of INFORMS devoted to the rapid publication of concise contributions in operations research Amsterdam [u.a.] (DE-627)ELV014727307 volume:48 year:2020 number:6 pages:777-783 extent:7 https://doi.org/10.1016/j.orl.2020.09.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 44.85 Kardiologie Angiologie VZ AR 48 2020 6 777-783 7 |
| allfieldsGer |
10.1016/j.orl.2020.09.008 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001201.pica (DE-627)ELV052004511 (ELSEVIER)S0167-6377(20)30146-2 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 44.85 bkl Ouorou, Adam verfasserin aut Fast proximal algorithms for nonsmooth convex optimization 2020 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In the lines of our previous approach to devise proximal algorithms for nonsmooth convex optimization by applying Nesterov fast gradient concept to the Moreau–Yosida regularization of a convex function, we develop three new proximal algorithms for nonsmooth convex optimization. In these algorithms, the errors in computing approximate solutions for the Moreau–Yosida regularization are not fixed beforehand, while preserving the complexity estimates already established. We report some preliminary computational results to give a first estimate of their performance. Nesterov accelerated gradient method Elsevier Proximal methods Elsevier Nonsmooth optimization Elsevier Convex programming Elsevier Enthalten in Elsevier Science Parikh, Neil U. ELSEVIER Impact of Lung Flute Therapy on Asthma: A Pilot Study 2017 a journal of INFORMS devoted to the rapid publication of concise contributions in operations research Amsterdam [u.a.] (DE-627)ELV014727307 volume:48 year:2020 number:6 pages:777-783 extent:7 https://doi.org/10.1016/j.orl.2020.09.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 44.85 Kardiologie Angiologie VZ AR 48 2020 6 777-783 7 |
| allfieldsSound |
10.1016/j.orl.2020.09.008 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001201.pica (DE-627)ELV052004511 (ELSEVIER)S0167-6377(20)30146-2 DE-627 ger DE-627 rakwb eng 610 VZ 610 VZ 44.85 bkl Ouorou, Adam verfasserin aut Fast proximal algorithms for nonsmooth convex optimization 2020 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In the lines of our previous approach to devise proximal algorithms for nonsmooth convex optimization by applying Nesterov fast gradient concept to the Moreau–Yosida regularization of a convex function, we develop three new proximal algorithms for nonsmooth convex optimization. In these algorithms, the errors in computing approximate solutions for the Moreau–Yosida regularization are not fixed beforehand, while preserving the complexity estimates already established. We report some preliminary computational results to give a first estimate of their performance. Nesterov accelerated gradient method Elsevier Proximal methods Elsevier Nonsmooth optimization Elsevier Convex programming Elsevier Enthalten in Elsevier Science Parikh, Neil U. ELSEVIER Impact of Lung Flute Therapy on Asthma: A Pilot Study 2017 a journal of INFORMS devoted to the rapid publication of concise contributions in operations research Amsterdam [u.a.] (DE-627)ELV014727307 volume:48 year:2020 number:6 pages:777-783 extent:7 https://doi.org/10.1016/j.orl.2020.09.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 44.85 Kardiologie Angiologie VZ AR 48 2020 6 777-783 7 |
| language |
English |
| source |
Enthalten in Impact of Lung Flute Therapy on Asthma: A Pilot Study Amsterdam [u.a.] volume:48 year:2020 number:6 pages:777-783 extent:7 |
| sourceStr |
Enthalten in Impact of Lung Flute Therapy on Asthma: A Pilot Study Amsterdam [u.a.] volume:48 year:2020 number:6 pages:777-783 extent:7 |
| format_phy_str_mv |
Article |
| bklname |
Kardiologie Angiologie |
| institution |
findex.gbv.de |
| topic_facet |
Nesterov accelerated gradient method Proximal methods Nonsmooth optimization Convex programming |
| dewey-raw |
610 |
| isfreeaccess_bool |
false |
| container_title |
Impact of Lung Flute Therapy on Asthma: A Pilot Study |
| authorswithroles_txt_mv |
Ouorou, Adam @@aut@@ |
| publishDateDaySort_date |
2020-01-01T00:00:00Z |
| hierarchy_top_id |
ELV014727307 |
| dewey-sort |
3610 |
| id |
ELV052004511 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV052004511</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230624181832.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210910s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.orl.2020.09.008</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001201.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV052004511</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0167-6377(20)30146-2</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.85</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ouorou, Adam</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Fast proximal algorithms for nonsmooth convex optimization</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">7</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In the lines of our previous approach to devise proximal algorithms for nonsmooth convex optimization by applying Nesterov fast gradient concept to the Moreau–Yosida regularization of a convex function, we develop three new proximal algorithms for nonsmooth convex optimization. In these algorithms, the errors in computing approximate solutions for the Moreau–Yosida regularization are not fixed beforehand, while preserving the complexity estimates already established. We report some preliminary computational results to give a first estimate of their performance.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Nesterov accelerated gradient method</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Proximal methods</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Nonsmooth optimization</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Convex programming</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Parikh, Neil U. ELSEVIER</subfield><subfield code="t">Impact of Lung Flute Therapy on Asthma: A Pilot Study</subfield><subfield code="d">2017</subfield><subfield code="d">a journal of INFORMS devoted to the rapid publication of concise contributions in operations research</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV014727307</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:48</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:6</subfield><subfield code="g">pages:777-783</subfield><subfield code="g">extent:7</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.orl.2020.09.008</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.85</subfield><subfield code="j">Kardiologie</subfield><subfield code="j">Angiologie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">48</subfield><subfield code="j">2020</subfield><subfield code="e">6</subfield><subfield code="h">777-783</subfield><subfield code="g">7</subfield></datafield></record></collection>
|
| author |
Ouorou, Adam |
| spellingShingle |
Ouorou, Adam ddc 610 bkl 44.85 Elsevier Nesterov accelerated gradient method Elsevier Proximal methods Elsevier Nonsmooth optimization Elsevier Convex programming Fast proximal algorithms for nonsmooth convex optimization |
| authorStr |
Ouorou, Adam |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV014727307 |
| format |
electronic Article |
| dewey-ones |
610 - Medicine & health |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
610 VZ 44.85 bkl Fast proximal algorithms for nonsmooth convex optimization Nesterov accelerated gradient method Elsevier Proximal methods Elsevier Nonsmooth optimization Elsevier Convex programming Elsevier |
| topic |
ddc 610 bkl 44.85 Elsevier Nesterov accelerated gradient method Elsevier Proximal methods Elsevier Nonsmooth optimization Elsevier Convex programming |
| topic_unstemmed |
ddc 610 bkl 44.85 Elsevier Nesterov accelerated gradient method Elsevier Proximal methods Elsevier Nonsmooth optimization Elsevier Convex programming |
| topic_browse |
ddc 610 bkl 44.85 Elsevier Nesterov accelerated gradient method Elsevier Proximal methods Elsevier Nonsmooth optimization Elsevier Convex programming |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| hierarchy_parent_title |
Impact of Lung Flute Therapy on Asthma: A Pilot Study |
| hierarchy_parent_id |
ELV014727307 |
| dewey-tens |
610 - Medicine & health |
| hierarchy_top_title |
Impact of Lung Flute Therapy on Asthma: A Pilot Study |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV014727307 |
| title |
Fast proximal algorithms for nonsmooth convex optimization |
| ctrlnum |
(DE-627)ELV052004511 (ELSEVIER)S0167-6377(20)30146-2 |
| title_full |
Fast proximal algorithms for nonsmooth convex optimization |
| author_sort |
Ouorou, Adam |
| journal |
Impact of Lung Flute Therapy on Asthma: A Pilot Study |
| journalStr |
Impact of Lung Flute Therapy on Asthma: A Pilot Study |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology |
| recordtype |
marc |
| publishDateSort |
2020 |
| contenttype_str_mv |
zzz |
| container_start_page |
777 |
| author_browse |
Ouorou, Adam |
| container_volume |
48 |
| physical |
7 |
| class |
610 VZ 44.85 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Ouorou, Adam |
| doi_str_mv |
10.1016/j.orl.2020.09.008 |
| dewey-full |
610 |
| title_sort |
fast proximal algorithms for nonsmooth convex optimization |
| title_auth |
Fast proximal algorithms for nonsmooth convex optimization |
| abstract |
In the lines of our previous approach to devise proximal algorithms for nonsmooth convex optimization by applying Nesterov fast gradient concept to the Moreau–Yosida regularization of a convex function, we develop three new proximal algorithms for nonsmooth convex optimization. In these algorithms, the errors in computing approximate solutions for the Moreau–Yosida regularization are not fixed beforehand, while preserving the complexity estimates already established. We report some preliminary computational results to give a first estimate of their performance. |
| abstractGer |
In the lines of our previous approach to devise proximal algorithms for nonsmooth convex optimization by applying Nesterov fast gradient concept to the Moreau–Yosida regularization of a convex function, we develop three new proximal algorithms for nonsmooth convex optimization. In these algorithms, the errors in computing approximate solutions for the Moreau–Yosida regularization are not fixed beforehand, while preserving the complexity estimates already established. We report some preliminary computational results to give a first estimate of their performance. |
| abstract_unstemmed |
In the lines of our previous approach to devise proximal algorithms for nonsmooth convex optimization by applying Nesterov fast gradient concept to the Moreau–Yosida regularization of a convex function, we develop three new proximal algorithms for nonsmooth convex optimization. In these algorithms, the errors in computing approximate solutions for the Moreau–Yosida regularization are not fixed beforehand, while preserving the complexity estimates already established. We report some preliminary computational results to give a first estimate of their performance. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 |
| container_issue |
6 |
| title_short |
Fast proximal algorithms for nonsmooth convex optimization |
| url |
https://doi.org/10.1016/j.orl.2020.09.008 |
| remote_bool |
true |
| ppnlink |
ELV014727307 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1016/j.orl.2020.09.008 |
| up_date |
2024-07-06T21:50:22.261Z |
| _version_ |
1803868042440998912 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV052004511</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230624181832.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210910s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.orl.2020.09.008</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001201.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV052004511</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0167-6377(20)30146-2</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.85</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ouorou, Adam</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Fast proximal algorithms for nonsmooth convex optimization</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">7</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In the lines of our previous approach to devise proximal algorithms for nonsmooth convex optimization by applying Nesterov fast gradient concept to the Moreau–Yosida regularization of a convex function, we develop three new proximal algorithms for nonsmooth convex optimization. In these algorithms, the errors in computing approximate solutions for the Moreau–Yosida regularization are not fixed beforehand, while preserving the complexity estimates already established. We report some preliminary computational results to give a first estimate of their performance.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Nesterov accelerated gradient method</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Proximal methods</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Nonsmooth optimization</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Convex programming</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Parikh, Neil U. ELSEVIER</subfield><subfield code="t">Impact of Lung Flute Therapy on Asthma: A Pilot Study</subfield><subfield code="d">2017</subfield><subfield code="d">a journal of INFORMS devoted to the rapid publication of concise contributions in operations research</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV014727307</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:48</subfield><subfield code="g">year:2020</subfield><subfield code="g">number:6</subfield><subfield code="g">pages:777-783</subfield><subfield code="g">extent:7</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.orl.2020.09.008</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">44.85</subfield><subfield code="j">Kardiologie</subfield><subfield code="j">Angiologie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">48</subfield><subfield code="j">2020</subfield><subfield code="e">6</subfield><subfield code="h">777-783</subfield><subfield code="g">7</subfield></datafield></record></collection>
|
| score |
7.3993254 |