Phase Retrieval with Sparse Phase Constraint

DOI10.1137/19M1266800zbMath1484.65129arXiv1804.01878OpenAlexW3013665705MaRDI QIDQ5027026

D. Russell Luke, Verhaegen, Michel, Oleg A. Soloviev, Nguyen Hieu Thao

Publication date: 3 February 2022

Published in: SIAM Journal on Mathematics of Data Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1804.01878

zbMATH Keywords

nonconvex optimizationlinear convergenceprojection algorithmprox-regularityphase retrievalsparsity constraint

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonconvex programming, global optimization (90C26) Diffraction, scattering (78A45) Numerical methods for discrete and fast Fourier transforms (65T50) Inverse problems (including inverse scattering) in optics and electromagnetic theory (78A46)

Related Items (4)

Wasserstein-Based Projections with Applications to Inverse Problems ⋮ Convex combination of alternating projection and Douglas-Rachford operators for phase retrieval ⋮ A learning-enhanced projection method for solving convex feasibility problems ⋮ Projection methods for high numerical aperture phase retrieval

Uses Software

• GESPAR

Cites Work

• Restricted normal cones and sparsity optimization with affine constraints
• About subtransversality of collections of sets
• A convergent relaxation of the Douglas-Rachford algorithm
• Quantitative characterizations of regularity properties of collections of sets
• Necessary conditions for linear convergence of iterated expansive, set-valued mappings
• Set regularities and feasibility problems
• About regularity of collections of sets
• Finding Best Approximation Pairs Relative to a Convex and Prox-Regular Set in a Hilbert Space
• Variational Analysis
• Local differentiability of distance functions
• Optical Wavefront Reconstruction: Theory and Numerical Methods
• GESPAR: Efficient Phase Retrieval of Sparse Signals
• Alternating Projections and Douglas-Rachford for Sparse Affine Feasibility
• On Fienup Methods for Sparse Phase Retrieval
• Quantitative Convergence Analysis of Iterated Expansive, Set-Valued Mappings
• Nonconvex Notions of Regularity and Convergence of Fundamental Algorithms for Feasibility Problems
• Implicit Functions and Solution Mappings

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