Convergence Analysis of Consensus-ADMM for General QCQP
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866917816905498624 |
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| author | Huang, Huiping So, Hing Cheung Zoubir, Abdelhak M. |
| author_facet | Huang, Huiping So, Hing Cheung Zoubir, Abdelhak M. |
| contents | We analyze the convergence properties of the consensus-alternating direction method of multipliers (ADMM) for solving general quadratically constrained quadratic programs. We prove that the augmented Lagrangian function value is monotonically non-increasing as long as the augmented Lagrangian parameter is chosen to be sufficiently large. Simulation results show that the augmented Lagrangian function is bounded from below when the matrix in the quadratic term of the objective function is positive definite. In such a case, the consensus-ADMM is convergent. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2205_14884 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Convergence Analysis of Consensus-ADMM for General QCQP Huang, Huiping So, Hing Cheung Zoubir, Abdelhak M. Signal Processing We analyze the convergence properties of the consensus-alternating direction method of multipliers (ADMM) for solving general quadratically constrained quadratic programs. We prove that the augmented Lagrangian function value is monotonically non-increasing as long as the augmented Lagrangian parameter is chosen to be sufficiently large. Simulation results show that the augmented Lagrangian function is bounded from below when the matrix in the quadratic term of the objective function is positive definite. In such a case, the consensus-ADMM is convergent. |
| title | Convergence Analysis of Consensus-ADMM for General QCQP |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2205.14884 |