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|---|---|---|---|
| 005 | 20260901030409.0 | ||
| 008 | 250101s2020 xx o 000 0 eng d | ||
| 100 | 1 | _aPeng Li | |
| 245 | 1 | 0 | _aInertial Proximal ADMM for Separable Multi-Block Convex Optimizations and Compressive Affine Phase Retrieval |
| 264 | 1 |
_barXiv _c2020 |
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| 336 | _atext | ||
| 338 | _aonline resource | ||
| 520 | _aSeparable multi-block convex optimization problem appears in many mathematical and engineering fields. In the first part of this paper, we propose an inertial proximal ADMM to solve a linearly constrained separable multi-block convex optimization problem, and we show that the proposed inertial proximal ADMM has global convergence under mild assumptions on the regularization matrices. Affine phase retrieval arises in holography, data separation and phaseless sampling, and it is also considered as a nonhomogeneous version of phase retrieval that has received considerable attention in recent years. Inspired by convex relaxation of vector sparsity and matrix rank in compressive sensing and by phase lifting in phase retrieval, in the second part of this paper, we introduce a compressive affine phase retrieval via lifting approach to connect affine phase retrieval with multi-block convex optim | ||
| 506 | 0 | _aOpen access — freely available to read. | |
| 856 | 4 | 0 |
_uhttps://arxiv.org/pdf/2012.13771v1 _yRead the full paper (PDF) |
| 942 | _cERES | ||
| 999 |
_c705 _d705 |
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