Solving PhaseLift by Low-Rank Riemannian Optimization Methods for Complex Semidefinite Constraints

DOI10.1137/16M1072838zbMath1373.65044OpenAlexW2757162727WikidataQ115246957 ScholiaQ115246957MaRDI QIDQ5358961

Wen Huang, Xiangxiong Zhang, Kyle A. Gallivan

Publication date: 22 September 2017

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/16m1072838

zbMATH Keywords

algorithmphase retrievalRiemannian optimizationphaseliftlow-rank optimizationHermitian positive semidefinite matricescomplex optimizationWirtinger flow algorithm

Mathematics Subject Classification ID

Numerical optimization and variational techniques (65K10) Inverse problems in optimal control (49N45) Problems with incomplete information (optimization) (49N30)

Related Items

The numerics of phase retrieval, An efficient damped Newton-type algorithm with globalization strategy on Riemannian manifolds, A limited-memory Riemannian symmetric rank-one trust-region method with a restart strategy, An Efficient and Robust Scalar Auxialiary Variable Based Algorithm for Discrete Gradient Systems Arising from Optimizations, Solving PhaseLift by Low-Rank Riemannian Optimization Methods for Complex Semidefinite Constraints, A Riemannian BFGS Method Without Differentiated Retraction for Nonconvex Optimization Problems, Intrinsic representation of tangent vectors and vector transports on matrix manifolds, Scalable incremental nonconvex optimization approach for phase retrieval, Quotient Geometry with Simple Geodesics for the Manifold of Fixed-Rank Positive-Semidefinite Matrices, Blind Deconvolution by a Steepest Descent Algorithm on a Quotient Manifold

Uses Software

• SDPT3
• FFTW
• SDPLR
• TFOCS
• ROPTLIB
• PhaseLift

Cites Work

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