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Exact(8)
Matrix Z can be recovered by solving MMV problem.
The raw image of the scene can be recovered by solving optimization problems.
Under the sparse spectrum assumption, the FRV can be recovered by solving the combinatorial optimization problem r ̂ = arg min r r 0 s.
For recovering the jointly-sparse signals X, a feasible way is that the row support of the jointly sparse signals is first determined and then the signals can be recovered by solving a least square (LS) problem [1].
It is known from [14] that whenever Φ satisfies the block-RIP with δ 2k < 1, there is a unique block-sparse signal x which can be recovered by solving the following problem: min x ∥ x ∥ 2, 0 s.t.
It is well known that the sparse signal (x_{0}) can be recovered by solving the following LASSO problem [36]: begin{aligned}[b] &min_{xin R^{n}} frac{1}{2}| Ax - b |_{2}^{2} &quadtext{s.t.}quad |x|_{1} leq t, end{aligned} (6.2) where (t >0).
Similar(52)
The underlying data is recovered by solving problem (BP ε ).
With the development of the compressive sensing (CS) theory, the sparse signals can be recovered exactly by solving a convex optimization problem [6].
It has been shown that the signal can be accurately recovered by solving the following convex minimization program: begin{array}{*{20}l} &min|hat{mathbf{x}}|_{1} &text{s.t.
Their bodies were recovered by divers.
The fumble was recovered by Desmond Bishop.
More suggestions(18)
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