Large-scale convex optimization: algorithms & analyses via monotone operators
Starting from where a first course in convex optimization leaves off, this text presents a unified analysis of first-order optimization methods – including parallel-distributed algorithms – through the abstraction of monotone operators. With the increased computational power and availability of big...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, United Kingdom
Cambridge University Press
2023
|
| Schlagworte: | |
| Online-Zugang: | TUM01 Volltext |
| Zusammenfassung: | Starting from where a first course in convex optimization leaves off, this text presents a unified analysis of first-order optimization methods – including parallel-distributed algorithms – through the abstraction of monotone operators. With the increased computational power and availability of big data over the past decade, applied disciplines have demanded that larger and larger optimization problems be solved. This text covers the first-order convex optimization methods that are uniquely effective at solving these large-scale optimization problems. Readers will have the opportunity to construct and analyze many well-known classical and modern algorithms using monotone operators, and walk away with a solid understanding of the diverse optimization algorithms. Graduate students and researchers in mathematical optimization, operations research, electrical engineering, statistics, and computer science will appreciate this concise introduction to the theory of convex optimization algorithms. |
| Beschreibung: | 1 Online-Ressource (xiv, 303 Seiten) |
| ISBN: | 9781009160865 |
| DOI: | 10.1017/9781009160865 |
Internformat
MARC
| LEADER | 00000nmm a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV048683373 | ||
| 003 | DE-604 | ||
| 005 | 00000000000000.0 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 230131s2023 |||| o||u| ||||||eng d | ||
| 020 | |a 9781009160865 |9 9781009160865 | ||
| 024 | 7 | |a 10.1017/9781009160865 |2 doi | |
| 035 | |a (OCoLC)1369556222 | ||
| 035 | |a (DE-599)BVBBV048683373 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-91 | ||
| 084 | |a SK 870 |0 (DE-625)143265: |2 rvk | ||
| 100 | 1 | |a Ryu, Ernest K. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Large-scale convex optimization |b algorithms & analyses via monotone operators |c Ernest K. Ryu (Seoul National University), Wotao Yin (University of California, Los Angeles, and DAMO Academy, Alibaba Group) |
| 264 | 1 | |a Cambridge, United Kingdom |b Cambridge University Press |c 2023 | |
| 300 | |a 1 Online-Ressource (xiv, 303 Seiten) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 520 | 3 | |a Starting from where a first course in convex optimization leaves off, this text presents a unified analysis of first-order optimization methods – including parallel-distributed algorithms – through the abstraction of monotone operators. With the increased computational power and availability of big data over the past decade, applied disciplines have demanded that larger and larger optimization problems be solved. This text covers the first-order convex optimization methods that are uniquely effective at solving these large-scale optimization problems. Readers will have the opportunity to construct and analyze many well-known classical and modern algorithms using monotone operators, and walk away with a solid understanding of the diverse optimization algorithms. Graduate students and researchers in mathematical optimization, operations research, electrical engineering, statistics, and computer science will appreciate this concise introduction to the theory of convex optimization algorithms. | |
| 650 | 0 | 7 | |a Monotoner Operator |0 (DE-588)4207161-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Optimierung |0 (DE-588)4043664-0 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Konvexe Menge |0 (DE-588)4165212-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Operatortheorie |0 (DE-588)4075665-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Modellierung |0 (DE-588)4170297-9 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Konvexe Funktion |0 (DE-588)4139679-0 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Modellierung |0 (DE-588)4170297-9 |D s |
| 689 | 0 | 1 | |a Konvexe Menge |0 (DE-588)4165212-5 |D s |
| 689 | 0 | 2 | |a Konvexe Funktion |0 (DE-588)4139679-0 |D s |
| 689 | 0 | 3 | |a Optimierung |0 (DE-588)4043664-0 |D s |
| 689 | 0 | 4 | |a Monotoner Operator |0 (DE-588)4207161-6 |D s |
| 689 | 0 | 5 | |a Operatortheorie |0 (DE-588)4075665-8 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Yin, Wotao |d ca. 20./21. Jh. |e Verfasser |0 (DE-588)103849625X |4 aut | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe, Hardcover |z 978-1-00-916085-8 |
| 856 | 4 | 0 | |u https://doi.org/10.1017/9781009160865 |x Verlag |z URL des Erstveröffentlichers |3 Volltext |
| 912 | |a ZDB-20-CBO | ||
| 999 | |a oai:aleph.bib-bvb.de:BVB01-034057705 | ||
| 966 | e | |u https://doi.org/10.1017/9781009160865 |l TUM01 |p ZDB-20-CBO |q TUM_Paketkauf_2022 |x Verlag |3 Volltext | |
Datensatz im Suchindex
| _version_ | 1804184857459294208 |
|---|---|
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Ryu, Ernest K. Yin, Wotao ca. 20./21. Jh |
| author_GND | (DE-588)103849625X |
| author_facet | Ryu, Ernest K. Yin, Wotao ca. 20./21. Jh |
| author_role | aut aut |
| author_sort | Ryu, Ernest K. |
| author_variant | e k r ek ekr w y wy |
| building | Verbundindex |
| bvnumber | BV048683373 |
| classification_rvk | SK 870 |
| collection | ZDB-20-CBO |
| ctrlnum | (OCoLC)1369556222 (DE-599)BVBBV048683373 |
| discipline | Mathematik |
| discipline_str_mv | Mathematik |
| doi_str_mv | 10.1017/9781009160865 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03159nmm a2200505 c 4500</leader><controlfield tag="001">BV048683373</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">00000000000000.0</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">230131s2023 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781009160865</subfield><subfield code="9">9781009160865</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1017/9781009160865</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1369556222</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV048683373</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">SK 870</subfield><subfield code="0">(DE-625)143265:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ryu, Ernest K.</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Large-scale convex optimization</subfield><subfield code="b">algorithms & analyses via monotone operators</subfield><subfield code="c">Ernest K. Ryu (Seoul National University), Wotao Yin (University of California, Los Angeles, and DAMO Academy, Alibaba Group)</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cambridge, United Kingdom</subfield><subfield code="b">Cambridge University Press</subfield><subfield code="c">2023</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (xiv, 303 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">Starting from where a first course in convex optimization leaves off, this text presents a unified analysis of first-order optimization methods – including parallel-distributed algorithms – through the abstraction of monotone operators. With the increased computational power and availability of big data over the past decade, applied disciplines have demanded that larger and larger optimization problems be solved. This text covers the first-order convex optimization methods that are uniquely effective at solving these large-scale optimization problems. Readers will have the opportunity to construct and analyze many well-known classical and modern algorithms using monotone operators, and walk away with a solid understanding of the diverse optimization algorithms. Graduate students and researchers in mathematical optimization, operations research, electrical engineering, statistics, and computer science will appreciate this concise introduction to the theory of convex optimization algorithms.</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Monotoner Operator</subfield><subfield code="0">(DE-588)4207161-6</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Optimierung</subfield><subfield code="0">(DE-588)4043664-0</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Konvexe Menge</subfield><subfield code="0">(DE-588)4165212-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Operatortheorie</subfield><subfield code="0">(DE-588)4075665-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Modellierung</subfield><subfield code="0">(DE-588)4170297-9</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Konvexe Funktion</subfield><subfield code="0">(DE-588)4139679-0</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Modellierung</subfield><subfield code="0">(DE-588)4170297-9</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Konvexe Menge</subfield><subfield code="0">(DE-588)4165212-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Konvexe Funktion</subfield><subfield code="0">(DE-588)4139679-0</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="3"><subfield code="a">Optimierung</subfield><subfield code="0">(DE-588)4043664-0</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="4"><subfield code="a">Monotoner Operator</subfield><subfield code="0">(DE-588)4207161-6</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="5"><subfield code="a">Operatortheorie</subfield><subfield code="0">(DE-588)4075665-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yin, Wotao</subfield><subfield code="d">ca. 20./21. Jh.</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)103849625X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe, Hardcover</subfield><subfield code="z">978-1-00-916085-8</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1017/9781009160865</subfield><subfield code="x">Verlag</subfield><subfield code="z">URL des Erstveröffentlichers</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-20-CBO</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-034057705</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/9781009160865</subfield><subfield code="l">TUM01</subfield><subfield code="p">ZDB-20-CBO</subfield><subfield code="q">TUM_Paketkauf_2022</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV048683373 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T21:25:52Z |
| indexdate | 2024-07-10T09:46:00Z |
| institution | BVB |
| isbn | 9781009160865 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034057705 |
| oclc_num | 1369556222 |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (xiv, 303 Seiten) |
| psigel | ZDB-20-CBO ZDB-20-CBO TUM_Paketkauf_2022 |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Cambridge University Press |
| record_format | marc |
| spelling | Ryu, Ernest K. Verfasser aut Large-scale convex optimization algorithms & analyses via monotone operators Ernest K. Ryu (Seoul National University), Wotao Yin (University of California, Los Angeles, and DAMO Academy, Alibaba Group) Cambridge, United Kingdom Cambridge University Press 2023 1 Online-Ressource (xiv, 303 Seiten) txt rdacontent c rdamedia cr rdacarrier Starting from where a first course in convex optimization leaves off, this text presents a unified analysis of first-order optimization methods – including parallel-distributed algorithms – through the abstraction of monotone operators. With the increased computational power and availability of big data over the past decade, applied disciplines have demanded that larger and larger optimization problems be solved. This text covers the first-order convex optimization methods that are uniquely effective at solving these large-scale optimization problems. Readers will have the opportunity to construct and analyze many well-known classical and modern algorithms using monotone operators, and walk away with a solid understanding of the diverse optimization algorithms. Graduate students and researchers in mathematical optimization, operations research, electrical engineering, statistics, and computer science will appreciate this concise introduction to the theory of convex optimization algorithms. Monotoner Operator (DE-588)4207161-6 gnd rswk-swf Optimierung (DE-588)4043664-0 gnd rswk-swf Konvexe Menge (DE-588)4165212-5 gnd rswk-swf Operatortheorie (DE-588)4075665-8 gnd rswk-swf Modellierung (DE-588)4170297-9 gnd rswk-swf Konvexe Funktion (DE-588)4139679-0 gnd rswk-swf Modellierung (DE-588)4170297-9 s Konvexe Menge (DE-588)4165212-5 s Konvexe Funktion (DE-588)4139679-0 s Optimierung (DE-588)4043664-0 s Monotoner Operator (DE-588)4207161-6 s Operatortheorie (DE-588)4075665-8 s DE-604 Yin, Wotao ca. 20./21. Jh. Verfasser (DE-588)103849625X aut Erscheint auch als Druck-Ausgabe, Hardcover 978-1-00-916085-8 https://doi.org/10.1017/9781009160865 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Ryu, Ernest K. Yin, Wotao ca. 20./21. Jh Large-scale convex optimization algorithms & analyses via monotone operators Monotoner Operator (DE-588)4207161-6 gnd Optimierung (DE-588)4043664-0 gnd Konvexe Menge (DE-588)4165212-5 gnd Operatortheorie (DE-588)4075665-8 gnd Modellierung (DE-588)4170297-9 gnd Konvexe Funktion (DE-588)4139679-0 gnd |
| subject_GND | (DE-588)4207161-6 (DE-588)4043664-0 (DE-588)4165212-5 (DE-588)4075665-8 (DE-588)4170297-9 (DE-588)4139679-0 |
| title | Large-scale convex optimization algorithms & analyses via monotone operators |
| title_auth | Large-scale convex optimization algorithms & analyses via monotone operators |
| title_exact_search | Large-scale convex optimization algorithms & analyses via monotone operators |
| title_exact_search_txtP | Large-scale convex optimization algorithms & analyses via monotone operators |
| title_full | Large-scale convex optimization algorithms & analyses via monotone operators Ernest K. Ryu (Seoul National University), Wotao Yin (University of California, Los Angeles, and DAMO Academy, Alibaba Group) |
| title_fullStr | Large-scale convex optimization algorithms & analyses via monotone operators Ernest K. Ryu (Seoul National University), Wotao Yin (University of California, Los Angeles, and DAMO Academy, Alibaba Group) |
| title_full_unstemmed | Large-scale convex optimization algorithms & analyses via monotone operators Ernest K. Ryu (Seoul National University), Wotao Yin (University of California, Los Angeles, and DAMO Academy, Alibaba Group) |
| title_short | Large-scale convex optimization |
| title_sort | large scale convex optimization algorithms analyses via monotone operators |
| title_sub | algorithms & analyses via monotone operators |
| topic | Monotoner Operator (DE-588)4207161-6 gnd Optimierung (DE-588)4043664-0 gnd Konvexe Menge (DE-588)4165212-5 gnd Operatortheorie (DE-588)4075665-8 gnd Modellierung (DE-588)4170297-9 gnd Konvexe Funktion (DE-588)4139679-0 gnd |
| topic_facet | Monotoner Operator Optimierung Konvexe Menge Operatortheorie Modellierung Konvexe Funktion |
| url | https://doi.org/10.1017/9781009160865 |
| work_keys_str_mv | AT ryuernestk largescaleconvexoptimizationalgorithmsanalysesviamonotoneoperators AT yinwotao largescaleconvexoptimizationalgorithmsanalysesviamonotoneoperators |