Distributed Optimization with Application to Power Systems and Control
Mathematical optimization techniques are among the most successful tools for controlling technical systems optimally with feasibility guarantees. Yet, they are often centralized—all data has to be collected in one central and computationally powerful entity. Methods from distributed optimization ove...
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| Year of Publication: | 2022 |
| Language: | English |
| Physical Description: | 1 electronic resource (226 p.) |
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| 100 | 1 | |a Engelmann, Alexander |4 auth | |
| 245 | 1 | 0 | |a Distributed Optimization with Application to Power Systems and Control |
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| 300 | |a 1 electronic resource (226 p.) | ||
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| 520 | |a Mathematical optimization techniques are among the most successful tools for controlling technical systems optimally with feasibility guarantees. Yet, they are often centralized—all data has to be collected in one central and computationally powerful entity. Methods from distributed optimization overcome this limitation. Classical approaches, however, are often not applicable due to non-convexities. This work develops one of the first frameworks for distributed non-convex optimization. | ||
| 546 | |a English | ||
| 650 | 7 | |a Maths for computer scientists |2 bicssc | |
| 653 | |a Verteilte Optimierung | ||
| 653 | |a Dezentrale Optimierung | ||
| 653 | |a ALADIN | ||
| 653 | |a ADMM | ||
| 653 | |a Optimal Power Flow | ||
| 653 | |a distributed optimization | ||
| 653 | |a decentralized optimization | ||
| 653 | |a optimal power flow | ||
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| 906 | |a BOOK | ||
| ADM | |b 2023-12-15 05:58:28 Europe/Vienna |f system |c marc21 |a 2022-12-05 16:10:45 Europe/Vienna |g false | ||
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