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In this paper, we propose {\it \underline{R}ecursive} {\it \underline{I}mportance} {\it \underline{S}ketching} algorithm for {\it \underline{R}ank} constrained least squares {\it \underline{O}ptimization} (RISRO).
Phase retrieval algorithms: a comparison
Fienup, J. R. (1982) · 1982
Earlier work this paper cites.
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Burke, J. V. (1985) · 1985
Earlier work this paper cites.
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Burer, S. and Monteiro, R. D. (2003) · 2003
Earlier work this paper cites.
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Candès, E. J., Li, X., and Soltanolkotabi, M. (2015) · 2007
Earlier work this paper cites.
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Absil, P.-A., Mahony, R., and Sepulchre, R. (2008) · 2008
Earlier work this paper cites.
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Candès, E. J. (2008) · 2008
Earlier work this paper cites.
Matrix completion from a few entries
Keshavan, R. H., Oh, S., and Montanari, A. (2009) · 2009
Earlier work this paper cites.
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Jain, P., Meka, R., and Dhillon, I. S. (2010) · 2010
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Projection-like retractions on matrix manifolds
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Earlier work this paper cites.
Fast approximation of matrix coherence and statistical leverage
Drineas, P., Magdon-Ismail, M., Mahoney, M. W., and Woodruff, D. P. (2012) · 2012
Earlier work this paper cites.
Iterative reweighted algorithms for matrix rank minimization
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Earlier work this paper cites.
Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm
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Earlier work this paper cites.
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Earlier work this paper cites.
Blind deconvolution using convex programming
Ahmed, A., Recht, B., and Romberg, J. (2013) · 2013
Earlier work this paper cites.
Sharp RIP bound for sparse signal and low-rank matrix recovery
Cai, T. T. and Zhang, A. (2013) · 2013
Earlier work this paper cites.
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In Introduction to Smooth Manifolds
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Phase retrieval using alternating minimization
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Normalized iterative hard thresholding for matrix completion
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A Riemannian rank-adaptive method for low-rank optimization
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Solving random quadratic systems of equations is nearly as easy as solving linear systems
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Newton sketch: A near linear-time optimization algorithm with linear-quadratic convergence
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Sketching as a tool for numerical linear algebra
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A geometric analysis of phase retrieval
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Asymptotics for sketching in least squares regression
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Solving (most) of a set of quadratic equalities: Composite optimization for robust phase retrieval
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Implicit regularization in nonconvex statistical estimation: Gradient descent converges linearly for phase retrieval, matrix completion, and blind deconvolution
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Sharp restricted isometry bounds for the inexistence of spurious local minima in nonconvex matrix recovery
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