A Linearly Convergent Algorithm for Solving a Class of Nonconvex/Affine Feasibility Problems
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Publication:2897274
DOI10.1007/978-1-4419-9569-8_3zbMath1242.90225OpenAlexW189211379MaRDI QIDQ2897274
Publication date: 10 July 2012
Published in: Springer Optimization and Its Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-1-4419-9569-8_3
inverse problemsgradient projection algorithmcompressive sensingsparse signal recoverylinear rate of convergenceaffine rank minimizationmutual coherence of a matrixnonconvex affine feasibilityscalable restricted isometry
Related Items
On linear convergence of projected gradient method for a class of affine rank minimization problems, On the complexity of solving feasibility problems with regularized models, Exact minimum rank approximation via Schatten \(p\)-norm minimization, Restricted normal cones and sparsity optimization with affine constraints, Prox-regularity of rank constraint sets and implications for algorithms, Matrix recipes for hard thresholding methods, Randomized Projection Methods for Convex Feasibility: Conditioning and Convergence Rates
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