Sentence examples for measurements by solving from inspiring English sources
The images are reconstructed from m = 0.4 N complex Fourier measurements by solving the ℓ1-minimization problem (5).
As the prediction error is often very sparse, it can be recovered from these measurements by solving the l-1 minimization.
The signal is then recovered from undersampled measurements by solving an inverse problem either through a linear program or a greedy pursuit [7].
Then, the dense components are fused by the selection method according to the manifestations of defocus, while the sparse components are fused under the frame of CS via fusing a few linear measurements by solving the problem of l1 norm minimization which is based on the two-step iterative shrinkage reconstruction algorithm.
Using the model, fluxes can be computed from the measurements by solving a nonlinear least-squares problem (NLSP).
For the setting presented in Section 1, the theory of compressed sensing already provides sufficient conditions on the number of measurements needed to recover the vector α from the measurements y by solving the ℓ1-minimization problem (2) [6, 7].
Performance evaluation is done by obtaining load test measurements and by solving the LQN model.
We add this tilting effect into both measurement methods by solving the migrating grain-boundary profiles for arbitrary θ.
The signal on the unit sphere that is consistent with the measurements is found by solving: begin{array}{*{20}l} &hat{mathbf{x}}=underset{mathbf{x}}{argmin}|mathbf{x}|_{1} &text{s.t.
In the analysis model, recovering x from the incomplete measurements is achieved by solving the following minimization problem [11]: widehat{boldsymbol{x}}=underset{boldsymbol{x}}{argmin}left|right|boldsymbol{Omega} boldsymbol{x}{left|right|}_0kern1em mathrm{s}.mathrm{t}.kern1em parallel boldsymbol{y}-boldsymbol{Mx}{parallel}_2le varepsilon (3).
where a sparse estimate of x ˆ is obtained from the measurement vector y by solving the following constrained optimization problem: x ^ Ω = arg min x ∈ ℂ G x 1 s.
