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On Local Convergence of the Method of Alternating Projections

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TLDR
In this article, the authors proved local convergence of alternating projections between subanalytic sets under a mild regularity hypothesis on one of the sets, and showed that the speed of convergence is O(k √ √ σ(k − σ ) for some constant σ √ n, σ (n) for some σ σ = (0, √ N) √ (n − ρ) for any σ > 0.
Abstract
The method of alternating projections is a classical tool to solve feasibility problems. Here we prove local convergence of alternating projections between subanalytic sets $$A,B$$A,B under a mild regularity hypothesis on one of the sets. We show that the speed of convergence is $${\mathcal {O}}(k^{-\rho })$$O(k-?) for some $$\rho \in (0,\infty )$$??(0,?).

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Alternating projections and coupling slope

TL;DR: In this article, a local linear convergence result that makes no regularity assumptions on either set (unlike previous results), while at the same time weakening standard transversal intersection assumptions is presented.
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A Lyapunov-type approach to convergence of the Douglas–Rachford algorithm for a nonconvex setting

TL;DR: In this paper, the authors prove convergence of the Douglas-Rachford algorithm in a potentially nonconvex setting by relying on the existence of a Lyapunov-type functional whose convexity properties are not tantamount to convexness of the original constraint sets.
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About subtransversality of collections of sets

TL;DR: In this article, the authors provide dual sufficient conditions for subtransversality of collections of sets in an Asplund space setting, and for the convex case, they formulate a necessary and sufficient dual criterion of sub-transversal in general Banach spaces.
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A Lyapunov-type approach to convergence of the Douglas-Rachford algorithm

TL;DR: In this paper, the authors prove convergence of the Douglas-Rachford algorithm in a potentially nonconvex setting by relying on the existence of a Lyapunov-type functional whose convexity properties are not tantamount to convexness of the original constraint sets.
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From Fienup's phase retrieval techniques to regularized inversion for in-line holography: tutorial.

TL;DR: This paper includes a tutorial on how to reconstruct in-line holograms using an inverse problems approach, starting with modeling the observations, selecting regularizations and constraints, and ending with the design of a reconstruction algorithm.
References
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Journal ArticleDOI

Phase retrieval algorithms: a comparison.

TL;DR: Iterative algorithms for phase retrieval from intensity data are compared to gradient search methods and it is shown that both the error-reduction algorithm for the problem of a single intensity measurement and the Gerchberg-Saxton algorithm forThe problem of two intensity measurements converge.
Journal Article

A practical algorithm for the determination of phase from image and diffraction plane pictures

R. W. Gerchberg
- 01 Jan 1972 - 
TL;DR: In this article, an algorithm is presented for the rapid solution of the phase of the complete wave function whose intensity in the diffraction and imaging planes of an imaging system are known.
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Splitting Algorithms for the Sum of Two Nonlinear Operators

TL;DR: This work studies two splitting algorithms for (stationary and evolution) problems involving the sum of two monotone operators with real-time requirements.
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Extending the methodology of X-ray crystallography to allow imaging of micrometre-sized non-crystalline specimens

TL;DR: Extending the methodology of X-ray crystallography to allow imaging of micrometre-sized non-crystalline specimens was proposed in this paper, where the authors extended the methodology to allow the imaging of micro-scale specimens.
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On Projection Algorithms for Solving Convex Feasibility Problems

TL;DR: A very broad and flexible framework is investigated which allows a systematic discussion of questions on behaviour in general Hilbert spaces and on the quality of convergence in convex feasibility problems.
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