Name
Abstract | ||
|---|---|---|
A framework, PhaseLift, was recently proposed to solve the phase retrieval problem. In this framework, the problem is solved by optimizing a cost function over the set of complex Hermitian positive semidefinite matrices. This paper considers an approach based on an alternative cost function defined on a union of appropriate manifolds. It is related to the original cost function in a manner that preserves the ability to find a global minimizer and is significantly more efficient computationally. A rank-based optimality condition for stationary points is given and optimization algorithms based on state-of-the-art Riemannian optimization and dynamically reducing rank are proposed. Empirical evaluations are performed using the PhaseLift problem. The new approach is shown to be an effective method of phase retrieval with computational efficiency increased substantially compared to the algorithm used in original PhaseLift paper. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.procs.2016.05.422 | ICCS |
Keywords | Field | DocType |
Riemannian optimization, Hermitian positive semidefinite, Riemannian quasi-Newton, Rank adaptive method | Mathematical optimization,Phase retrieval,Matrix (mathematics),Computer science,Effective method,Positive-definite matrix,Riemannian optimization,Stationary point,Hermitian matrix,Manifold | Conference |
Volume | Issue | ISSN |
80 | C | 1877-0509 |
Citations | PageRank | References |
2 | 0.37 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wen Huang | 1 | 77 | 8.07 |
| Kyle Gallivan | 2 | 889 | 154.22 |
| Xiangxiong Zhang | 3 | 462 | 32.93 |