Results 271 to 280 of about 1,734,778 (295)

Weighted compressed sensing and rank minimization

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011
We present an alternative analysis of weighted l 1 minimization for sparse signals with a nonuniform sparsity model, and extend our results to nuclear norm minimization for matrices with nonuniform singular vector distribution. In the case of vectors, we find explicit upper bounds for the successful recovery thresholds, and give a simple suboptimal ...
Oymak, Samet, Khajehnejad, M. Amin, Hassibi, Babak   +2 more
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On Minimal Odd Rankings for Büchi Complementation

2009
We study minimal odd rankings (as defined by Kupferman and Vardi[KV01]) for run-DAGs of words in the complement of a nondeterministic Buchi automaton. We present an optimized version of the ranking based complementation construction of Friedgut, Kupferman and Vardi[FKV06] and Schewe's[Sch09] variant of it, such that every accepting run of the ...
Hrishikesh Karmarkar, Supratik Chakraborty   +1 more
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Further results on minimal rankings

Ars Comb., 1999
A \(k\)-ranking of a graph is a coloring of its vertices by integers from \(\{1,2,\dots ,k\}\) such that for any two vertices colored by the same color, each path connecting these two vertices contains a vertex colored by a higher color. A \(k\)-ranking is minimal if recoloring any single vertex by a lower color results in a coloring which is no more a
J. Ghoshal, Renu C. Laskar, Dan Pillone
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Rank minimization over finite fields

2011 IEEE International Symposium on Information Theory Proceedings, 2011
This paper establishes information-theoretic limits in estimating a finite field low-rank matrix given random linear measurements of it. Necessary and sufficient conditions on the number of measurements required are provided. It is shown that these conditions are sharp.
Vincent Y. F. Tan, Laura Balzano, Stark C. Draper   +2 more
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Rank Minimization with Structured Data Patterns

2014
The problem of finding a low rank approximation of a given measurement matrix is of key interest in computer vision. If all the elements of the measurement matrix are available, the problem can be solved using factorization. However, in the case of missing data no satisfactory solution exists.
Viktor Larsson, Carl Olsson, Erik Bylow, Fredrik Kahl   +3 more
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Iterative rank minimization for broadcast beamforming

2014 22nd Signal Processing and Communications Applications Conference (SIU), 2014
Optimization problem of broadcast beamforming is nonconvex due to rank condition. Semidefinite relaxation (SDR) method is proposed to solve this problem. However, in certain cases, the performance of SDR becomes unacceptable. In this paper, an equivalent formulation of the original problem without rank condition is found.
Ozlem Tugfe Demir, T. Engin Tuncer
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An iterative approach to Rank Minimization Problems

2015 54th IEEE Conference on Decision and Control (CDC), 2015
This paper investigates an iterative approach to solve the Rank Minimization Problems (RMPs) constrained in a convex set. The matrix rank function is discontinuous and nonconvex and the general RMP is classified as NP-hard. A continuous function is firstly introduced to approximately represent the matrix rank function with prescribed accuracy by ...
Chuangchuang Sun, Ran Dai
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Fractional Minimal Rank

Linear and Multilinear Algebra, 2021
Ben Grossmann, Hugo Jan Woerdeman
exaly  

On Topological Rank of Factors of Cantor Minimal Systems

Ergodic Theory and Dynamical Systems, 2022
Nasser Golestani
exaly  

Minimal k-rankings and the rank number of

Information Processing Letters, 2009
Sarah Novotny, Juan Ortiz, Darren A. Narayan   +2 more
openaire   +1 more source