Convex optimization in signal processing and communications:
Over the past two decades there have been significant advances in the field of optimization. In particular, convex optimization has emerged as a powerful signal processing tool, and the variety of applications continues to grow rapidly. This book, written by a team of leading experts, sets out the t...
Gespeichert in:
| Weitere Verfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2011
|
| Schlagworte: | |
| Online-Zugang: | DE-12 DE-92 DE-29 URL des Erstveröffentlichers |
| Zusammenfassung: | Over the past two decades there have been significant advances in the field of optimization. In particular, convex optimization has emerged as a powerful signal processing tool, and the variety of applications continues to grow rapidly. This book, written by a team of leading experts, sets out the theoretical underpinnings of the subject and provides tutorials on a wide range of convex optimization applications. Emphasis throughout is on cutting-edge research and on formulating problems in convex form, making this an ideal textbook for advanced graduate courses and a useful self-study guide. Topics covered range from automatic code generation, graphical models, and gradient-based algorithms for signal recovery, to semidefinite programming (SDP) relaxation and radar waveform design via SDP. It also includes blind source separation for image processing, robust broadband beamforming, distributed multi-agent optimization for networked systems, cognitive radio systems via game theory, and the variational inequality approach for Nash equilibrium solutions |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 05 Oct 2015) |
| Beschreibung: | 1 online resource (xiv, 498 pages) |
| ISBN: | 9780511804458 |
| DOI: | 10.1017/CBO9780511804458 |
Internformat
MARC
| LEADER | 00000nam a2200000zc 4500 | ||
|---|---|---|---|
| 001 | BV043944683 | ||
| 003 | DE-604 | ||
| 005 | 20190723 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 161206s2011 xx o|||| 00||| eng d | ||
| 020 | |a 9780511804458 |c Online |9 978-0-511-80445-8 | ||
| 024 | 7 | |a 10.1017/CBO9780511804458 |2 doi | |
| 035 | |a (ZDB-20-CBO)CR9780511804458 | ||
| 035 | |a (OCoLC)851068722 | ||
| 035 | |a (DE-599)BVBBV043944683 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-12 |a DE-29 |a DE-92 | ||
| 082 | 0 | |a 621.3822015196 |2 22 | |
| 084 | |a ZN 6025 |0 (DE-625)157494: |2 rvk | ||
| 245 | 1 | 0 | |a Convex optimization in signal processing and communications |c edited by Daniel P. Palomar and Yonina C. Eldar |
| 246 | 1 | 3 | |a Convex Optimization in Signal Processing & Communications |
| 264 | 1 | |a Cambridge |b Cambridge University Press |c 2011 | |
| 300 | |a 1 online resource (xiv, 498 pages) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 500 | |a Title from publisher's bibliographic system (viewed on 05 Oct 2015) | ||
| 520 | |a Over the past two decades there have been significant advances in the field of optimization. In particular, convex optimization has emerged as a powerful signal processing tool, and the variety of applications continues to grow rapidly. This book, written by a team of leading experts, sets out the theoretical underpinnings of the subject and provides tutorials on a wide range of convex optimization applications. Emphasis throughout is on cutting-edge research and on formulating problems in convex form, making this an ideal textbook for advanced graduate courses and a useful self-study guide. Topics covered range from automatic code generation, graphical models, and gradient-based algorithms for signal recovery, to semidefinite programming (SDP) relaxation and radar waveform design via SDP. It also includes blind source separation for image processing, robust broadband beamforming, distributed multi-agent optimization for networked systems, cognitive radio systems via game theory, and the variational inequality approach for Nash equilibrium solutions | ||
| 650 | 4 | |a Signal processing | |
| 650 | 4 | |a Mathematical optimization | |
| 650 | 4 | |a Convex functions | |
| 650 | 0 | 7 | |a Signalverarbeitung |0 (DE-588)4054947-1 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Konvexe Funktion |0 (DE-588)4139679-0 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Optimierung |0 (DE-588)4043664-0 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Signalverarbeitung |0 (DE-588)4054947-1 |D s |
| 689 | 0 | 1 | |a Optimierung |0 (DE-588)4043664-0 |D s |
| 689 | 0 | 2 | |a Konvexe Funktion |0 (DE-588)4139679-0 |D s |
| 689 | 0 | |8 1\p |5 DE-604 | |
| 700 | 1 | |a Palomar, Daniel P. |4 edt | |
| 700 | 1 | |a Eldar, Yonina C. |d 1973- |0 (DE-588)1043849696 |4 edt | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe, Hardcover |z 978-0-521-76222-9 |
| 856 | 4 | 0 | |u https://doi.org/10.1017/CBO9780511804458 |x Verlag |z URL des Erstveröffentlichers |3 Volltext |
| 883 | 1 | |8 1\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
| 912 | |a ZDB-20-CBO | ||
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-029353654 | |
| 966 | e | |u https://doi.org/10.1017/CBO9780511804458 |l DE-12 |p ZDB-20-CBO |q BSB_PDA_CBO |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1017/CBO9780511804458 |l DE-92 |p ZDB-20-CBO |q FHN_PDA_CBO |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1017/CBO9780511804458 |l DE-29 |p ZDB-20-CBO |q UER_PDA_CBO_Kauf |x Verlag |3 Volltext | |
Datensatz im Suchindex
| _version_ | 1822792509882892288 |
|---|---|
| adam_text | |
| any_adam_object | |
| author2 | Palomar, Daniel P. Eldar, Yonina C. 1973- |
| author2_role | edt edt |
| author2_variant | d p p dp dpp y c e yc yce |
| author_GND | (DE-588)1043849696 |
| author_facet | Palomar, Daniel P. Eldar, Yonina C. 1973- |
| building | Verbundindex |
| bvnumber | BV043944683 |
| classification_rvk | ZN 6025 |
| collection | ZDB-20-CBO |
| ctrlnum | (ZDB-20-CBO)CR9780511804458 (OCoLC)851068722 (DE-599)BVBBV043944683 |
| dewey-full | 621.3822015196 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 621 - Applied physics |
| dewey-raw | 621.3822015196 |
| dewey-search | 621.3822015196 |
| dewey-sort | 3621.3822015196 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Elektrotechnik / Elektronik / Nachrichtentechnik |
| doi_str_mv | 10.1017/CBO9780511804458 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nam a2200000zc 4500</leader><controlfield tag="001">BV043944683</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20190723</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">161206s2011 xx o|||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780511804458</subfield><subfield code="c">Online</subfield><subfield code="9">978-0-511-80445-8</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1017/CBO9780511804458</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-20-CBO)CR9780511804458</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)851068722</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV043944683</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-12</subfield><subfield code="a">DE-29</subfield><subfield code="a">DE-92</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">621.3822015196</subfield><subfield code="2">22</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ZN 6025</subfield><subfield code="0">(DE-625)157494:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Convex optimization in signal processing and communications</subfield><subfield code="c">edited by Daniel P. Palomar and Yonina C. Eldar</subfield></datafield><datafield tag="246" ind1="1" ind2="3"><subfield code="a">Convex Optimization in Signal Processing & Communications</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cambridge</subfield><subfield code="b">Cambridge University Press</subfield><subfield code="c">2011</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (xiv, 498 pages)</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="500" ind1=" " ind2=" "><subfield code="a">Title from publisher's bibliographic system (viewed on 05 Oct 2015)</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Over the past two decades there have been significant advances in the field of optimization. In particular, convex optimization has emerged as a powerful signal processing tool, and the variety of applications continues to grow rapidly. This book, written by a team of leading experts, sets out the theoretical underpinnings of the subject and provides tutorials on a wide range of convex optimization applications. Emphasis throughout is on cutting-edge research and on formulating problems in convex form, making this an ideal textbook for advanced graduate courses and a useful self-study guide. Topics covered range from automatic code generation, graphical models, and gradient-based algorithms for signal recovery, to semidefinite programming (SDP) relaxation and radar waveform design via SDP. It also includes blind source separation for image processing, robust broadband beamforming, distributed multi-agent optimization for networked systems, cognitive radio systems via game theory, and the variational inequality approach for Nash equilibrium solutions</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Signal processing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mathematical optimization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Convex functions</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Signalverarbeitung</subfield><subfield code="0">(DE-588)4054947-1</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="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="689" ind1="0" ind2="0"><subfield code="a">Signalverarbeitung</subfield><subfield code="0">(DE-588)4054947-1</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><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="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=" "><subfield code="8">1\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Palomar, Daniel P.</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Eldar, Yonina C.</subfield><subfield code="d">1973-</subfield><subfield code="0">(DE-588)1043849696</subfield><subfield code="4">edt</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">978-0-521-76222-9</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1017/CBO9780511804458</subfield><subfield code="x">Verlag</subfield><subfield code="z">URL des Erstveröffentlichers</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">1\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-20-CBO</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-029353654</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/CBO9780511804458</subfield><subfield code="l">DE-12</subfield><subfield code="p">ZDB-20-CBO</subfield><subfield code="q">BSB_PDA_CBO</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/CBO9780511804458</subfield><subfield code="l">DE-92</subfield><subfield code="p">ZDB-20-CBO</subfield><subfield code="q">FHN_PDA_CBO</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/CBO9780511804458</subfield><subfield code="l">DE-29</subfield><subfield code="p">ZDB-20-CBO</subfield><subfield code="q">UER_PDA_CBO_Kauf</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV043944683 |
| illustrated | Not Illustrated |
| indexdate | 2025-01-31T19:06:40Z |
| institution | BVB |
| isbn | 9780511804458 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029353654 |
| oclc_num | 851068722 |
| open_access_boolean | |
| owner | DE-12 DE-29 DE-92 |
| owner_facet | DE-12 DE-29 DE-92 |
| physical | 1 online resource (xiv, 498 pages) |
| psigel | ZDB-20-CBO ZDB-20-CBO BSB_PDA_CBO ZDB-20-CBO FHN_PDA_CBO ZDB-20-CBO UER_PDA_CBO_Kauf |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | Cambridge University Press |
| record_format | marc |
| spelling | Convex optimization in signal processing and communications edited by Daniel P. Palomar and Yonina C. Eldar Convex Optimization in Signal Processing & Communications Cambridge Cambridge University Press 2011 1 online resource (xiv, 498 pages) txt rdacontent c rdamedia cr rdacarrier Title from publisher's bibliographic system (viewed on 05 Oct 2015) Over the past two decades there have been significant advances in the field of optimization. In particular, convex optimization has emerged as a powerful signal processing tool, and the variety of applications continues to grow rapidly. This book, written by a team of leading experts, sets out the theoretical underpinnings of the subject and provides tutorials on a wide range of convex optimization applications. Emphasis throughout is on cutting-edge research and on formulating problems in convex form, making this an ideal textbook for advanced graduate courses and a useful self-study guide. Topics covered range from automatic code generation, graphical models, and gradient-based algorithms for signal recovery, to semidefinite programming (SDP) relaxation and radar waveform design via SDP. It also includes blind source separation for image processing, robust broadband beamforming, distributed multi-agent optimization for networked systems, cognitive radio systems via game theory, and the variational inequality approach for Nash equilibrium solutions Signal processing Mathematical optimization Convex functions Signalverarbeitung (DE-588)4054947-1 gnd rswk-swf Konvexe Funktion (DE-588)4139679-0 gnd rswk-swf Optimierung (DE-588)4043664-0 gnd rswk-swf Signalverarbeitung (DE-588)4054947-1 s Optimierung (DE-588)4043664-0 s Konvexe Funktion (DE-588)4139679-0 s 1\p DE-604 Palomar, Daniel P. edt Eldar, Yonina C. 1973- (DE-588)1043849696 edt Erscheint auch als Druck-Ausgabe, Hardcover 978-0-521-76222-9 https://doi.org/10.1017/CBO9780511804458 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Convex optimization in signal processing and communications Signal processing Mathematical optimization Convex functions Signalverarbeitung (DE-588)4054947-1 gnd Konvexe Funktion (DE-588)4139679-0 gnd Optimierung (DE-588)4043664-0 gnd |
| subject_GND | (DE-588)4054947-1 (DE-588)4139679-0 (DE-588)4043664-0 |
| title | Convex optimization in signal processing and communications |
| title_alt | Convex Optimization in Signal Processing & Communications |
| title_auth | Convex optimization in signal processing and communications |
| title_exact_search | Convex optimization in signal processing and communications |
| title_full | Convex optimization in signal processing and communications edited by Daniel P. Palomar and Yonina C. Eldar |
| title_fullStr | Convex optimization in signal processing and communications edited by Daniel P. Palomar and Yonina C. Eldar |
| title_full_unstemmed | Convex optimization in signal processing and communications edited by Daniel P. Palomar and Yonina C. Eldar |
| title_short | Convex optimization in signal processing and communications |
| title_sort | convex optimization in signal processing and communications |
| topic | Signal processing Mathematical optimization Convex functions Signalverarbeitung (DE-588)4054947-1 gnd Konvexe Funktion (DE-588)4139679-0 gnd Optimierung (DE-588)4043664-0 gnd |
| topic_facet | Signal processing Mathematical optimization Convex functions Signalverarbeitung Konvexe Funktion Optimierung |
| url | https://doi.org/10.1017/CBO9780511804458 |
| work_keys_str_mv | AT palomardanielp convexoptimizationinsignalprocessingandcommunications AT eldaryoninac convexoptimizationinsignalprocessingandcommunications AT palomardanielp convexoptimizationinsignalprocessingcommunications AT eldaryoninac convexoptimizationinsignalprocessingcommunications |