M
Maryam Fazel
Researcher at University of Washington
Publications - 126
Citations - 12889
Maryam Fazel is an academic researcher from University of Washington. The author has contributed to research in topics: Convex optimization & Computer science. The author has an hindex of 32, co-authored 106 publications receiving 11676 citations. Previous affiliations of Maryam Fazel include Stanford University & Northeastern University.
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Journal ArticleDOI
Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
TL;DR: It is shown that if a certain restricted isometry property holds for the linear transformation defining the constraints, the minimum-rank solution can be recovered by solving a convex optimization problem, namely, the minimization of the nuclear norm over the given affine space.
Journal Article
Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
TL;DR: In this paper, it was shown that if a certain restricted isometry property holds for the linear transformation defining the constraints, the minimum-rank solution can be recovered by solving a convex optimization problem, namely, the minimization of the nuclear norm over the given affine space.
Proceedings ArticleDOI
A rank minimization heuristic with application to minimum order system approximation
TL;DR: It is shown that the heuristic to replace the (nonconvex) rank objective with the sum of the singular values of the matrix, which is the dual of the spectral norm, can be reduced to a semidefinite program, hence efficiently solved.
Proceedings ArticleDOI
Log-det heuristic for matrix rank minimization with applications to Hankel and Euclidean distance matrices
TL;DR: A heuristic for minimizing the rank of a positive semidefinite matrix over a convex set using the logarithm of the determinant as a smooth approximation for rank is presented and readily extended to handle general matrices.
Journal ArticleDOI
Hankel Matrix Rank Minimization with Applications to System Identification and Realization
TL;DR: A flexible optimization framework for nuclear norm minimization of matrices with linear structure, including Hankel, Toeplitz, and moment structures and catalog applications from diverse fields under this framework is introduced.