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...
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| Weitere Verfasser: | , |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge ; New York :
Cambridge University Press,
©2010.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| 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: | 1 online resource (xiv, 498 pages) : illustrations |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9780511691232 0511691238 9780511692352 0511692358 9780511804458 0511804458 1107208122 9781107208124 1282653261 9781282653269 9786612653261 6612653264 0511689756 9780511689758 0511690495 9780511690495 0511689004 9780511689000 |
Internformat
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| 505 | 0 | |a 1. Automatic code generation for real-time convex optimization / Jacob Mattingley and Stephen Boyd -- 2. Gradient-based algorithmswith applications to signal-recovery problems / Amir Beck and Marc Teboulle -- 3. Graphical models of autoregressive processes / Jitkomut Songsiri, Joachim Dahl and Lieven Vandenberghe -- 4. SDP relaxation of homogeneous quadratic optimization: approximation bounds and applications / Zhi-Quan Luo and Tsung-Hui Chang -- 5. Probabilistic analysis of semidefinite relaxation detectors for multiple-input, multiple-output systems / Anthony Man-Cho So and Yinyu Ye -- 6. Semidefinite programming, matrix decomposition, and radar code design / Yongwei Huang, Antonio De Maio and Shuzhong Zhang -- 7. Convex analysis for non-negative blind source separation with application in imaging / Wing-Kin Ma, Tsung-Han Chan, Chong-Yung Chi and Vue Wang -- 8. Optimization techniques in modern sampling theory / Tomer Michaeli and Yonina C. Eldar -- 9. Robust broadband adaptive beamforming using convex optimization / Michael Rubsamen, Amr El-Keyi, Alex B. Gershman and Thia Kirubarajan -- 10. Cooperative distributed multi-agentoptimization / Angelia Nedic and Asuman Ozdaglar -- 11. Competitive optimization of cognitive radio MIMO systems via game theory / Gesualso Scutari, Daniel P. Palomar and Sergio Barbarossa -- 12. Nash equilibria: the variational approach / Francisco Facchinei and Jong-Shi Pang. | |
| 588 | 0 | |a Print version record. | |
| 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. | ||
| 546 | |a English. | ||
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| 650 | 0 | |a Mathematical optimization. |0 http://id.loc.gov/authorities/subjects/sh85082127 | |
| 650 | 0 | |a Convex functions. |0 http://id.loc.gov/authorities/subjects/sh85031728 | |
| 650 | 6 | |a Traitement du signal. | |
| 650 | 6 | |a Optimisation mathématique. | |
| 650 | 6 | |a Fonctions convexes. | |
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| 650 | 7 | |a Mathematical optimization |2 fast | |
| 650 | 7 | |a Signal processing |2 fast | |
| 655 | 0 | |a Electronic book. | |
| 655 | 4 | |a Electronic books. | |
| 700 | 1 | |a Palomar, Daniel P. |0 http://id.loc.gov/authorities/names/no2007146184 | |
| 700 | 1 | |a Eldar, Yonina C. |0 http://id.loc.gov/authorities/names/no2010062198 | |
| 758 | |i has work: |a Convex optimization in signal processing and communications (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGkqRwVtmhhdhhtQ6Mdb3P |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
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| _version_ | 1816881732101603328 |
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| author2 | Palomar, Daniel P. Eldar, Yonina C. |
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| contents | 1. Automatic code generation for real-time convex optimization / Jacob Mattingley and Stephen Boyd -- 2. Gradient-based algorithmswith applications to signal-recovery problems / Amir Beck and Marc Teboulle -- 3. Graphical models of autoregressive processes / Jitkomut Songsiri, Joachim Dahl and Lieven Vandenberghe -- 4. SDP relaxation of homogeneous quadratic optimization: approximation bounds and applications / Zhi-Quan Luo and Tsung-Hui Chang -- 5. Probabilistic analysis of semidefinite relaxation detectors for multiple-input, multiple-output systems / Anthony Man-Cho So and Yinyu Ye -- 6. Semidefinite programming, matrix decomposition, and radar code design / Yongwei Huang, Antonio De Maio and Shuzhong Zhang -- 7. Convex analysis for non-negative blind source separation with application in imaging / Wing-Kin Ma, Tsung-Han Chan, Chong-Yung Chi and Vue Wang -- 8. Optimization techniques in modern sampling theory / Tomer Michaeli and Yonina C. Eldar -- 9. Robust broadband adaptive beamforming using convex optimization / Michael Rubsamen, Amr El-Keyi, Alex B. Gershman and Thia Kirubarajan -- 10. Cooperative distributed multi-agentoptimization / Angelia Nedic and Asuman Ozdaglar -- 11. Competitive optimization of cognitive radio MIMO systems via game theory / Gesualso Scutari, Daniel P. Palomar and Sergio Barbarossa -- 12. Nash equilibria: the variational approach / Francisco Facchinei and Jong-Shi Pang. |
| ctrlnum | (OCoLC)650341993 |
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| 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 |
| format | Electronic eBook |
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| genre | Electronic book. Electronic books. |
| genre_facet | Electronic book. Electronic books. |
| id | ZDB-4-EBA-ocn650341993 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:17:23Z |
| institution | BVB |
| isbn | 9780511691232 0511691238 9780511692352 0511692358 9780511804458 0511804458 1107208122 9781107208124 1282653261 9781282653269 9786612653261 6612653264 0511689756 9780511689758 0511690495 9780511690495 0511689004 9780511689000 |
| language | English |
| oclc_num | 650341993 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xiv, 498 pages) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2010 |
| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | Cambridge University Press, |
| record_format | marc |
| spelling | Convex optimization in signal processing and communications / edited by Daniel P. Palomar and Yonina C. Eldar. Cambridge ; New York : Cambridge University Press, ©2010. 1 online resource (xiv, 498 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references and index. 1. Automatic code generation for real-time convex optimization / Jacob Mattingley and Stephen Boyd -- 2. Gradient-based algorithmswith applications to signal-recovery problems / Amir Beck and Marc Teboulle -- 3. Graphical models of autoregressive processes / Jitkomut Songsiri, Joachim Dahl and Lieven Vandenberghe -- 4. SDP relaxation of homogeneous quadratic optimization: approximation bounds and applications / Zhi-Quan Luo and Tsung-Hui Chang -- 5. Probabilistic analysis of semidefinite relaxation detectors for multiple-input, multiple-output systems / Anthony Man-Cho So and Yinyu Ye -- 6. Semidefinite programming, matrix decomposition, and radar code design / Yongwei Huang, Antonio De Maio and Shuzhong Zhang -- 7. Convex analysis for non-negative blind source separation with application in imaging / Wing-Kin Ma, Tsung-Han Chan, Chong-Yung Chi and Vue Wang -- 8. Optimization techniques in modern sampling theory / Tomer Michaeli and Yonina C. Eldar -- 9. Robust broadband adaptive beamforming using convex optimization / Michael Rubsamen, Amr El-Keyi, Alex B. Gershman and Thia Kirubarajan -- 10. Cooperative distributed multi-agentoptimization / Angelia Nedic and Asuman Ozdaglar -- 11. Competitive optimization of cognitive radio MIMO systems via game theory / Gesualso Scutari, Daniel P. Palomar and Sergio Barbarossa -- 12. Nash equilibria: the variational approach / Francisco Facchinei and Jong-Shi Pang. Print version record. 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. English. Signal processing. http://id.loc.gov/authorities/subjects/sh85122397 Mathematical optimization. http://id.loc.gov/authorities/subjects/sh85082127 Convex functions. http://id.loc.gov/authorities/subjects/sh85031728 Traitement du signal. Optimisation mathématique. Fonctions convexes. COMPUTERS Information Theory. bisacsh TECHNOLOGY & ENGINEERING Signals & Signal Processing. bisacsh Convex functions fast Mathematical optimization fast Signal processing fast Electronic book. Electronic books. Palomar, Daniel P. http://id.loc.gov/authorities/names/no2007146184 Eldar, Yonina C. http://id.loc.gov/authorities/names/no2010062198 has work: Convex optimization in signal processing and communications (Text) https://id.oclc.org/worldcat/entity/E39PCGkqRwVtmhhdhhtQ6Mdb3P https://id.oclc.org/worldcat/ontology/hasWork Print version: Convex optimization in signal processing and communications. Cambridge, UK ; New York : Cambridge University Press, 2010 9780521762229 (OCoLC)422765059 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=325190 Volltext |
| spellingShingle | Convex optimization in signal processing and communications / 1. Automatic code generation for real-time convex optimization / Jacob Mattingley and Stephen Boyd -- 2. Gradient-based algorithmswith applications to signal-recovery problems / Amir Beck and Marc Teboulle -- 3. Graphical models of autoregressive processes / Jitkomut Songsiri, Joachim Dahl and Lieven Vandenberghe -- 4. SDP relaxation of homogeneous quadratic optimization: approximation bounds and applications / Zhi-Quan Luo and Tsung-Hui Chang -- 5. Probabilistic analysis of semidefinite relaxation detectors for multiple-input, multiple-output systems / Anthony Man-Cho So and Yinyu Ye -- 6. Semidefinite programming, matrix decomposition, and radar code design / Yongwei Huang, Antonio De Maio and Shuzhong Zhang -- 7. Convex analysis for non-negative blind source separation with application in imaging / Wing-Kin Ma, Tsung-Han Chan, Chong-Yung Chi and Vue Wang -- 8. Optimization techniques in modern sampling theory / Tomer Michaeli and Yonina C. Eldar -- 9. Robust broadband adaptive beamforming using convex optimization / Michael Rubsamen, Amr El-Keyi, Alex B. Gershman and Thia Kirubarajan -- 10. Cooperative distributed multi-agentoptimization / Angelia Nedic and Asuman Ozdaglar -- 11. Competitive optimization of cognitive radio MIMO systems via game theory / Gesualso Scutari, Daniel P. Palomar and Sergio Barbarossa -- 12. Nash equilibria: the variational approach / Francisco Facchinei and Jong-Shi Pang. Signal processing. http://id.loc.gov/authorities/subjects/sh85122397 Mathematical optimization. http://id.loc.gov/authorities/subjects/sh85082127 Convex functions. http://id.loc.gov/authorities/subjects/sh85031728 Traitement du signal. Optimisation mathématique. Fonctions convexes. COMPUTERS Information Theory. bisacsh TECHNOLOGY & ENGINEERING Signals & Signal Processing. bisacsh Convex functions fast Mathematical optimization fast Signal processing fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85122397 http://id.loc.gov/authorities/subjects/sh85082127 http://id.loc.gov/authorities/subjects/sh85031728 |
| title | Convex optimization in signal processing and 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. http://id.loc.gov/authorities/subjects/sh85122397 Mathematical optimization. http://id.loc.gov/authorities/subjects/sh85082127 Convex functions. http://id.loc.gov/authorities/subjects/sh85031728 Traitement du signal. Optimisation mathématique. Fonctions convexes. COMPUTERS Information Theory. bisacsh TECHNOLOGY & ENGINEERING Signals & Signal Processing. bisacsh Convex functions fast Mathematical optimization fast Signal processing fast |
| topic_facet | Signal processing. Mathematical optimization. Convex functions. Traitement du signal. Optimisation mathématique. Fonctions convexes. COMPUTERS Information Theory. TECHNOLOGY & ENGINEERING Signals & Signal Processing. Convex functions Mathematical optimization Signal processing Electronic book. Electronic books. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=325190 |
| work_keys_str_mv | AT palomardanielp convexoptimizationinsignalprocessingandcommunications AT eldaryoninac convexoptimizationinsignalprocessingandcommunications |