Parallel alternating direction method of multipliers
In this paper, we consider the distributed optimization problem, where the objective function is the sum of local cost functions. To solve this problem, a new parallel Alternating Direction Method of Multipliers (ADMM) algorithm is developed, which guarantees that the agents cooperatively reach an o...
Ausführliche Beschreibung
Autor*in: |
Yan, Jiaqi [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2020transfer abstract |
---|
Umfang: |
12 |
---|
Übergeordnetes Werk: |
Enthalten in: Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study - Petrruzziello, Carmelina ELSEVIER, 2013, an international journal, New York, NY |
---|---|
Übergeordnetes Werk: |
volume:507 ; year:2020 ; pages:185-196 ; extent:12 |
Links: |
---|
DOI / URN: |
10.1016/j.ins.2019.08.039 |
---|
Katalog-ID: |
ELV047818514 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | ELV047818514 | ||
003 | DE-627 | ||
005 | 20230626020533.0 | ||
007 | cr uuu---uuuuu | ||
008 | 191023s2020 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1016/j.ins.2019.08.039 |2 doi | |
028 | 5 | 2 | |a GBV00000000000739.pica |
035 | |a (DE-627)ELV047818514 | ||
035 | |a (ELSEVIER)S0020-0255(19)30779-0 | ||
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 570 |q VZ |
084 | |a BIODIV |q DE-30 |2 fid | ||
084 | |a 35.70 |2 bkl | ||
084 | |a 42.12 |2 bkl | ||
084 | |a 42.15 |2 bkl | ||
100 | 1 | |a Yan, Jiaqi |e verfasserin |4 aut | |
245 | 1 | 0 | |a Parallel alternating direction method of multipliers |
264 | 1 | |c 2020transfer abstract | |
300 | |a 12 | ||
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 this paper, we consider the distributed optimization problem, where the objective function is the sum of local cost functions. To solve this problem, a new parallel Alternating Direction Method of Multipliers (ADMM) algorithm is developed, which guarantees that the agents cooperatively reach an optimal agreement. Different from most of the existing ADMM approaches, our algorithm allows all the agents to update their local variables simultaneously in a parallel manner. It is theoretically proved that the local solutions of all the agents could reach a consensus, and converge to the optimal solution asymptotically with the rate of O(1/k). Numerical examples are finally provided to validate the effectiveness of the proposed method. | ||
520 | |a In this paper, we consider the distributed optimization problem, where the objective function is the sum of local cost functions. To solve this problem, a new parallel Alternating Direction Method of Multipliers (ADMM) algorithm is developed, which guarantees that the agents cooperatively reach an optimal agreement. Different from most of the existing ADMM approaches, our algorithm allows all the agents to update their local variables simultaneously in a parallel manner. It is theoretically proved that the local solutions of all the agents could reach a consensus, and converge to the optimal solution asymptotically with the rate of O(1/k). Numerical examples are finally provided to validate the effectiveness of the proposed method. | ||
700 | 1 | |a Guo, Fanghong |4 oth | |
700 | 1 | |a Wen, Changyun |4 oth | |
700 | 1 | |a Li, Guoqi |4 oth | |
773 | 0 | 8 | |i Enthalten in |n Elsevier Science Inc |a Petrruzziello, Carmelina ELSEVIER |t Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study |d 2013 |d an international journal |g New York, NY |w (DE-627)ELV011843691 |
773 | 1 | 8 | |g volume:507 |g year:2020 |g pages:185-196 |g extent:12 |
856 | 4 | 0 | |u https://doi.org/10.1016/j.ins.2019.08.039 |3 Volltext |
912 | |a GBV_USEFLAG_U | ||
912 | |a GBV_ELV | ||
912 | |a SYSFLAG_U | ||
912 | |a FID-BIODIV | ||
912 | |a SSG-OLC-PHA | ||
936 | b | k | |a 35.70 |j Biochemie: Allgemeines |q VZ |
936 | b | k | |a 42.12 |j Biophysik |q VZ |
936 | b | k | |a 42.15 |j Zellbiologie |q VZ |
951 | |a AR | ||
952 | |d 507 |j 2020 |h 185-196 |g 12 |
author_variant |
j y jy |
---|---|
matchkey_str |
yanjiaqiguofanghongwenchangyunliguoqi:2020----:aalllentndrcineh |
hierarchy_sort_str |
2020transfer abstract |
bklnumber |
35.70 42.12 42.15 |
publishDate |
2020 |
allfields |
10.1016/j.ins.2019.08.039 doi GBV00000000000739.pica (DE-627)ELV047818514 (ELSEVIER)S0020-0255(19)30779-0 DE-627 ger DE-627 rakwb eng 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Yan, Jiaqi verfasserin aut Parallel alternating direction method of multipliers 2020transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, we consider the distributed optimization problem, where the objective function is the sum of local cost functions. To solve this problem, a new parallel Alternating Direction Method of Multipliers (ADMM) algorithm is developed, which guarantees that the agents cooperatively reach an optimal agreement. Different from most of the existing ADMM approaches, our algorithm allows all the agents to update their local variables simultaneously in a parallel manner. It is theoretically proved that the local solutions of all the agents could reach a consensus, and converge to the optimal solution asymptotically with the rate of O(1/k). Numerical examples are finally provided to validate the effectiveness of the proposed method. In this paper, we consider the distributed optimization problem, where the objective function is the sum of local cost functions. To solve this problem, a new parallel Alternating Direction Method of Multipliers (ADMM) algorithm is developed, which guarantees that the agents cooperatively reach an optimal agreement. Different from most of the existing ADMM approaches, our algorithm allows all the agents to update their local variables simultaneously in a parallel manner. It is theoretically proved that the local solutions of all the agents could reach a consensus, and converge to the optimal solution asymptotically with the rate of O(1/k). Numerical examples are finally provided to validate the effectiveness of the proposed method. Guo, Fanghong oth Wen, Changyun oth Li, Guoqi oth Enthalten in Elsevier Science Inc Petrruzziello, Carmelina ELSEVIER Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study 2013 an international journal New York, NY (DE-627)ELV011843691 volume:507 year:2020 pages:185-196 extent:12 https://doi.org/10.1016/j.ins.2019.08.039 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 507 2020 185-196 12 |
spelling |
10.1016/j.ins.2019.08.039 doi GBV00000000000739.pica (DE-627)ELV047818514 (ELSEVIER)S0020-0255(19)30779-0 DE-627 ger DE-627 rakwb eng 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Yan, Jiaqi verfasserin aut Parallel alternating direction method of multipliers 2020transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, we consider the distributed optimization problem, where the objective function is the sum of local cost functions. To solve this problem, a new parallel Alternating Direction Method of Multipliers (ADMM) algorithm is developed, which guarantees that the agents cooperatively reach an optimal agreement. Different from most of the existing ADMM approaches, our algorithm allows all the agents to update their local variables simultaneously in a parallel manner. It is theoretically proved that the local solutions of all the agents could reach a consensus, and converge to the optimal solution asymptotically with the rate of O(1/k). Numerical examples are finally provided to validate the effectiveness of the proposed method. In this paper, we consider the distributed optimization problem, where the objective function is the sum of local cost functions. To solve this problem, a new parallel Alternating Direction Method of Multipliers (ADMM) algorithm is developed, which guarantees that the agents cooperatively reach an optimal agreement. Different from most of the existing ADMM approaches, our algorithm allows all the agents to update their local variables simultaneously in a parallel manner. It is theoretically proved that the local solutions of all the agents could reach a consensus, and converge to the optimal solution asymptotically with the rate of O(1/k). Numerical examples are finally provided to validate the effectiveness of the proposed method. Guo, Fanghong oth Wen, Changyun oth Li, Guoqi oth Enthalten in Elsevier Science Inc Petrruzziello, Carmelina ELSEVIER Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study 2013 an international journal New York, NY (DE-627)ELV011843691 volume:507 year:2020 pages:185-196 extent:12 https://doi.org/10.1016/j.ins.2019.08.039 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 507 2020 185-196 12 |
allfields_unstemmed |
10.1016/j.ins.2019.08.039 doi GBV00000000000739.pica (DE-627)ELV047818514 (ELSEVIER)S0020-0255(19)30779-0 DE-627 ger DE-627 rakwb eng 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Yan, Jiaqi verfasserin aut Parallel alternating direction method of multipliers 2020transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, we consider the distributed optimization problem, where the objective function is the sum of local cost functions. To solve this problem, a new parallel Alternating Direction Method of Multipliers (ADMM) algorithm is developed, which guarantees that the agents cooperatively reach an optimal agreement. Different from most of the existing ADMM approaches, our algorithm allows all the agents to update their local variables simultaneously in a parallel manner. It is theoretically proved that the local solutions of all the agents could reach a consensus, and converge to the optimal solution asymptotically with the rate of O(1/k). Numerical examples are finally provided to validate the effectiveness of the proposed method. In this paper, we consider the distributed optimization problem, where the objective function is the sum of local cost functions. To solve this problem, a new parallel Alternating Direction Method of Multipliers (ADMM) algorithm is developed, which guarantees that the agents cooperatively reach an optimal agreement. Different from most of the existing ADMM approaches, our algorithm allows all the agents to update their local variables simultaneously in a parallel manner. It is theoretically proved that the local solutions of all the agents could reach a consensus, and converge to the optimal solution asymptotically with the rate of O(1/k). Numerical examples are finally provided to validate the effectiveness of the proposed method. Guo, Fanghong oth Wen, Changyun oth Li, Guoqi oth Enthalten in Elsevier Science Inc Petrruzziello, Carmelina ELSEVIER Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study 2013 an international journal New York, NY (DE-627)ELV011843691 volume:507 year:2020 pages:185-196 extent:12 https://doi.org/10.1016/j.ins.2019.08.039 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 507 2020 185-196 12 |
allfieldsGer |
10.1016/j.ins.2019.08.039 doi GBV00000000000739.pica (DE-627)ELV047818514 (ELSEVIER)S0020-0255(19)30779-0 DE-627 ger DE-627 rakwb eng 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Yan, Jiaqi verfasserin aut Parallel alternating direction method of multipliers 2020transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, we consider the distributed optimization problem, where the objective function is the sum of local cost functions. To solve this problem, a new parallel Alternating Direction Method of Multipliers (ADMM) algorithm is developed, which guarantees that the agents cooperatively reach an optimal agreement. Different from most of the existing ADMM approaches, our algorithm allows all the agents to update their local variables simultaneously in a parallel manner. It is theoretically proved that the local solutions of all the agents could reach a consensus, and converge to the optimal solution asymptotically with the rate of O(1/k). Numerical examples are finally provided to validate the effectiveness of the proposed method. In this paper, we consider the distributed optimization problem, where the objective function is the sum of local cost functions. To solve this problem, a new parallel Alternating Direction Method of Multipliers (ADMM) algorithm is developed, which guarantees that the agents cooperatively reach an optimal agreement. Different from most of the existing ADMM approaches, our algorithm allows all the agents to update their local variables simultaneously in a parallel manner. It is theoretically proved that the local solutions of all the agents could reach a consensus, and converge to the optimal solution asymptotically with the rate of O(1/k). Numerical examples are finally provided to validate the effectiveness of the proposed method. Guo, Fanghong oth Wen, Changyun oth Li, Guoqi oth Enthalten in Elsevier Science Inc Petrruzziello, Carmelina ELSEVIER Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study 2013 an international journal New York, NY (DE-627)ELV011843691 volume:507 year:2020 pages:185-196 extent:12 https://doi.org/10.1016/j.ins.2019.08.039 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 507 2020 185-196 12 |
allfieldsSound |
10.1016/j.ins.2019.08.039 doi GBV00000000000739.pica (DE-627)ELV047818514 (ELSEVIER)S0020-0255(19)30779-0 DE-627 ger DE-627 rakwb eng 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Yan, Jiaqi verfasserin aut Parallel alternating direction method of multipliers 2020transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In this paper, we consider the distributed optimization problem, where the objective function is the sum of local cost functions. To solve this problem, a new parallel Alternating Direction Method of Multipliers (ADMM) algorithm is developed, which guarantees that the agents cooperatively reach an optimal agreement. Different from most of the existing ADMM approaches, our algorithm allows all the agents to update their local variables simultaneously in a parallel manner. It is theoretically proved that the local solutions of all the agents could reach a consensus, and converge to the optimal solution asymptotically with the rate of O(1/k). Numerical examples are finally provided to validate the effectiveness of the proposed method. In this paper, we consider the distributed optimization problem, where the objective function is the sum of local cost functions. To solve this problem, a new parallel Alternating Direction Method of Multipliers (ADMM) algorithm is developed, which guarantees that the agents cooperatively reach an optimal agreement. Different from most of the existing ADMM approaches, our algorithm allows all the agents to update their local variables simultaneously in a parallel manner. It is theoretically proved that the local solutions of all the agents could reach a consensus, and converge to the optimal solution asymptotically with the rate of O(1/k). Numerical examples are finally provided to validate the effectiveness of the proposed method. Guo, Fanghong oth Wen, Changyun oth Li, Guoqi oth Enthalten in Elsevier Science Inc Petrruzziello, Carmelina ELSEVIER Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study 2013 an international journal New York, NY (DE-627)ELV011843691 volume:507 year:2020 pages:185-196 extent:12 https://doi.org/10.1016/j.ins.2019.08.039 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 507 2020 185-196 12 |
language |
English |
source |
Enthalten in Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study New York, NY volume:507 year:2020 pages:185-196 extent:12 |
sourceStr |
Enthalten in Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study New York, NY volume:507 year:2020 pages:185-196 extent:12 |
format_phy_str_mv |
Article |
bklname |
Biochemie: Allgemeines Biophysik Zellbiologie |
institution |
findex.gbv.de |
dewey-raw |
610 |
isfreeaccess_bool |
false |
container_title |
Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study |
authorswithroles_txt_mv |
Yan, Jiaqi @@aut@@ Guo, Fanghong @@oth@@ Wen, Changyun @@oth@@ Li, Guoqi @@oth@@ |
publishDateDaySort_date |
2020-01-01T00:00:00Z |
hierarchy_top_id |
ELV011843691 |
dewey-sort |
3610 |
id |
ELV047818514 |
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">ELV047818514</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626020533.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">191023s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.ins.2019.08.039</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBV00000000000739.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV047818514</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0020-0255(19)30779-0</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">570</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BIODIV</subfield><subfield code="q">DE-30</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.70</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.12</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.15</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Yan, Jiaqi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Parallel alternating direction method of multipliers</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">12</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 this paper, we consider the distributed optimization problem, where the objective function is the sum of local cost functions. To solve this problem, a new parallel Alternating Direction Method of Multipliers (ADMM) algorithm is developed, which guarantees that the agents cooperatively reach an optimal agreement. Different from most of the existing ADMM approaches, our algorithm allows all the agents to update their local variables simultaneously in a parallel manner. It is theoretically proved that the local solutions of all the agents could reach a consensus, and converge to the optimal solution asymptotically with the rate of O(1/k). Numerical examples are finally provided to validate the effectiveness of the proposed method.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In this paper, we consider the distributed optimization problem, where the objective function is the sum of local cost functions. To solve this problem, a new parallel Alternating Direction Method of Multipliers (ADMM) algorithm is developed, which guarantees that the agents cooperatively reach an optimal agreement. Different from most of the existing ADMM approaches, our algorithm allows all the agents to update their local variables simultaneously in a parallel manner. It is theoretically proved that the local solutions of all the agents could reach a consensus, and converge to the optimal solution asymptotically with the rate of O(1/k). Numerical examples are finally provided to validate the effectiveness of the proposed method.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guo, Fanghong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wen, Changyun</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Guoqi</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science Inc</subfield><subfield code="a">Petrruzziello, Carmelina ELSEVIER</subfield><subfield code="t">Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study</subfield><subfield code="d">2013</subfield><subfield code="d">an international journal</subfield><subfield code="g">New York, NY</subfield><subfield code="w">(DE-627)ELV011843691</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:507</subfield><subfield code="g">year:2020</subfield><subfield code="g">pages:185-196</subfield><subfield code="g">extent:12</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.ins.2019.08.039</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">FID-BIODIV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.70</subfield><subfield code="j">Biochemie: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.12</subfield><subfield code="j">Biophysik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.15</subfield><subfield code="j">Zellbiologie</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">507</subfield><subfield code="j">2020</subfield><subfield code="h">185-196</subfield><subfield code="g">12</subfield></datafield></record></collection>
|
author |
Yan, Jiaqi |
spellingShingle |
Yan, Jiaqi ddc 610 ddc 570 fid BIODIV bkl 35.70 bkl 42.12 bkl 42.15 Parallel alternating direction method of multipliers |
authorStr |
Yan, Jiaqi |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV011843691 |
format |
electronic Article |
dewey-ones |
610 - Medicine & health 570 - Life sciences; biology |
delete_txt_mv |
keep |
author_role |
aut |
collection |
elsevier |
remote_str |
true |
illustrated |
Not Illustrated |
topic_title |
610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Parallel alternating direction method of multipliers |
topic |
ddc 610 ddc 570 fid BIODIV bkl 35.70 bkl 42.12 bkl 42.15 |
topic_unstemmed |
ddc 610 ddc 570 fid BIODIV bkl 35.70 bkl 42.12 bkl 42.15 |
topic_browse |
ddc 610 ddc 570 fid BIODIV bkl 35.70 bkl 42.12 bkl 42.15 |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
zu |
author2_variant |
f g fg c w cw g l gl |
hierarchy_parent_title |
Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study |
hierarchy_parent_id |
ELV011843691 |
dewey-tens |
610 - Medicine & health 570 - Life sciences; biology |
hierarchy_top_title |
Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study |
isfreeaccess_txt |
false |
familylinks_str_mv |
(DE-627)ELV011843691 |
title |
Parallel alternating direction method of multipliers |
ctrlnum |
(DE-627)ELV047818514 (ELSEVIER)S0020-0255(19)30779-0 |
title_full |
Parallel alternating direction method of multipliers |
author_sort |
Yan, Jiaqi |
journal |
Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study |
journalStr |
Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study |
lang_code |
eng |
isOA_bool |
false |
dewey-hundreds |
600 - Technology 500 - Science |
recordtype |
marc |
publishDateSort |
2020 |
contenttype_str_mv |
zzz |
container_start_page |
185 |
author_browse |
Yan, Jiaqi |
container_volume |
507 |
physical |
12 |
class |
610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl |
format_se |
Elektronische Aufsätze |
author-letter |
Yan, Jiaqi |
doi_str_mv |
10.1016/j.ins.2019.08.039 |
dewey-full |
610 570 |
title_sort |
parallel alternating direction method of multipliers |
title_auth |
Parallel alternating direction method of multipliers |
abstract |
In this paper, we consider the distributed optimization problem, where the objective function is the sum of local cost functions. To solve this problem, a new parallel Alternating Direction Method of Multipliers (ADMM) algorithm is developed, which guarantees that the agents cooperatively reach an optimal agreement. Different from most of the existing ADMM approaches, our algorithm allows all the agents to update their local variables simultaneously in a parallel manner. It is theoretically proved that the local solutions of all the agents could reach a consensus, and converge to the optimal solution asymptotically with the rate of O(1/k). Numerical examples are finally provided to validate the effectiveness of the proposed method. |
abstractGer |
In this paper, we consider the distributed optimization problem, where the objective function is the sum of local cost functions. To solve this problem, a new parallel Alternating Direction Method of Multipliers (ADMM) algorithm is developed, which guarantees that the agents cooperatively reach an optimal agreement. Different from most of the existing ADMM approaches, our algorithm allows all the agents to update their local variables simultaneously in a parallel manner. It is theoretically proved that the local solutions of all the agents could reach a consensus, and converge to the optimal solution asymptotically with the rate of O(1/k). Numerical examples are finally provided to validate the effectiveness of the proposed method. |
abstract_unstemmed |
In this paper, we consider the distributed optimization problem, where the objective function is the sum of local cost functions. To solve this problem, a new parallel Alternating Direction Method of Multipliers (ADMM) algorithm is developed, which guarantees that the agents cooperatively reach an optimal agreement. Different from most of the existing ADMM approaches, our algorithm allows all the agents to update their local variables simultaneously in a parallel manner. It is theoretically proved that the local solutions of all the agents could reach a consensus, and converge to the optimal solution asymptotically with the rate of O(1/k). Numerical examples are finally provided to validate the effectiveness of the proposed method. |
collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA |
title_short |
Parallel alternating direction method of multipliers |
url |
https://doi.org/10.1016/j.ins.2019.08.039 |
remote_bool |
true |
author2 |
Guo, Fanghong Wen, Changyun Li, Guoqi |
author2Str |
Guo, Fanghong Wen, Changyun Li, Guoqi |
ppnlink |
ELV011843691 |
mediatype_str_mv |
z |
isOA_txt |
false |
hochschulschrift_bool |
false |
author2_role |
oth oth oth |
doi_str |
10.1016/j.ins.2019.08.039 |
up_date |
2024-07-06T17:12:00.186Z |
_version_ |
1803850529048100864 |
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">ELV047818514</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626020533.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">191023s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.ins.2019.08.039</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBV00000000000739.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV047818514</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0020-0255(19)30779-0</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">570</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BIODIV</subfield><subfield code="q">DE-30</subfield><subfield code="2">fid</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">35.70</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.12</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.15</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Yan, Jiaqi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Parallel alternating direction method of multipliers</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">12</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 this paper, we consider the distributed optimization problem, where the objective function is the sum of local cost functions. To solve this problem, a new parallel Alternating Direction Method of Multipliers (ADMM) algorithm is developed, which guarantees that the agents cooperatively reach an optimal agreement. Different from most of the existing ADMM approaches, our algorithm allows all the agents to update their local variables simultaneously in a parallel manner. It is theoretically proved that the local solutions of all the agents could reach a consensus, and converge to the optimal solution asymptotically with the rate of O(1/k). Numerical examples are finally provided to validate the effectiveness of the proposed method.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In this paper, we consider the distributed optimization problem, where the objective function is the sum of local cost functions. To solve this problem, a new parallel Alternating Direction Method of Multipliers (ADMM) algorithm is developed, which guarantees that the agents cooperatively reach an optimal agreement. Different from most of the existing ADMM approaches, our algorithm allows all the agents to update their local variables simultaneously in a parallel manner. It is theoretically proved that the local solutions of all the agents could reach a consensus, and converge to the optimal solution asymptotically with the rate of O(1/k). Numerical examples are finally provided to validate the effectiveness of the proposed method.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guo, Fanghong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wen, Changyun</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Guoqi</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science Inc</subfield><subfield code="a">Petrruzziello, Carmelina ELSEVIER</subfield><subfield code="t">Mo1264 Clinical Characteristics of Inflammatory Bowel Disease May Influence the Cancer Risk When Using Immunomodulators: Incident Cases of Cancer in a Multicenter Case-Control Study</subfield><subfield code="d">2013</subfield><subfield code="d">an international journal</subfield><subfield code="g">New York, NY</subfield><subfield code="w">(DE-627)ELV011843691</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:507</subfield><subfield code="g">year:2020</subfield><subfield code="g">pages:185-196</subfield><subfield code="g">extent:12</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.ins.2019.08.039</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">FID-BIODIV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.70</subfield><subfield code="j">Biochemie: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.12</subfield><subfield code="j">Biophysik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">42.15</subfield><subfield code="j">Zellbiologie</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">507</subfield><subfield code="j">2020</subfield><subfield code="h">185-196</subfield><subfield code="g">12</subfield></datafield></record></collection>
|
score |
7.4007263 |