2017
DOI: 10.1109/access.2017.2657645
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Dynamic Magnetic Resonance Imaging via Nonconvex Low-Rank Matrix Approximation

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Cited by 23 publications
(12 citation statements)
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“…Iteration Equation (9) in Theorem 1 can be used to compute the dual sequence {ω t }, since the matrix A has full row rank and the objective function is strictly convex. The matrix A in this problem has different forms with it in NUM, and therefore predetermined routes are not needed when we rewrite Equation (9). Theorem 5.…”
Section: Distributed Newton Methods For Multi-agent Problems In Generamentioning
confidence: 99%
See 2 more Smart Citations
“…Iteration Equation (9) in Theorem 1 can be used to compute the dual sequence {ω t }, since the matrix A has full row rank and the objective function is strictly convex. The matrix A in this problem has different forms with it in NUM, and therefore predetermined routes are not needed when we rewrite Equation (9). Theorem 5.…”
Section: Distributed Newton Methods For Multi-agent Problems In Generamentioning
confidence: 99%
“…Finally, we obtain the desired distributed iteration (20) when substituting Equations (A4)-(A6) into (9).…”
Section: Appendix Bmentioning
confidence: 99%
See 1 more Smart Citation
“…where ( ( ( +1) + ( +1) ) − ) represents artifacts, which is subtracted from the updating ( +1) + ( +1) as a residual, resulting in a clearer image [22].…”
Section: Modelingmentioning
confidence: 99%
“…In addition, authors of [20,22] pointed out that, among common nonconvex functions, such as -norm [23], SCAD [24], log [15], MCP [25], ETP [26], and Geman [27], -norm is the best approximation to 0 norm. Inspired by this, we propose a new model as follows:…”
Section: Modelingmentioning
confidence: 99%