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This paper concerns a fundamental class of convex matrix optimization problems.
An algorithm for quadratic programming
M. Frank and P. Wolfe · 1956
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Minimization methods in the presence of constraints
E. S. Levitin and B. T. Poljak · 1966
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Monotone operators and the proximal point algorithm
R. T. Rockafellar · 1976
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Phase retrieval algorithms: a comparison
J. R. Fienup · 1982
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Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming
M. X. Goemans and D. P. Williamson · 1995
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A spectral bundle method for semidefinite programming
C. Helmberg and F. Rendl · 2000
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M. Fazel · 2002
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A nonlinear programming algorithm for solving semidefinite programs via low-rank factorization
S. Burer and R. D. C. Monteiro · 2003
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Maximum-margin matrix factorizations
N. Srebro, J. Rennie, and T. Jaakkola · 2004
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Interior gradient and proximal methods for convex and conic optimization
A. Auslender and M. Teboulle · 2006
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Improved approximation algorithms for large matrices via random projections
T. Sarlós · 2006
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Sparse approximate solutions to semidefinite programs
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Painless reconstruction from magnitudes of frame coefficients
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Convergence rates of sub-sampled Newton methods
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Phase retrieval using alternating minimization
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Second-order stochastic optimization in linear time
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The non-convex Burer-Monteiro approach works on smooth semidefinite programs
N. Boumal, V. Voroninski, and A.S. Bandeira · 2016
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Frank-Wolfe works for non-Lipschitz continuous gradient objectives: scalable Poisson phase retrieval
G. Odor, Y.-H. Li, A. Yurtsever, Y.-P. Hsieh, Q. Tran-Dinh, M. El Halabi, and V. Cevher · 2016
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Randomized single-view algorithms for low-rank matrix reconstruction
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