The algorithm permits to solve alternatively, at each iteration, the equations for the velocity field and for the thickness.
We then introduce a shifted block FOM algorithm with deflated restarting for solving these alternatively shifted linear systems efficiently.
In this paper, we propose to solve some alternatively shifted linear systems.
Firstly, by introducing the tight frame constraint into the popular dictionary learning model, the proposed tight frame learning model could be formulated as a nonconvex optimization problem which can be solved by alternatively implementing hard thresholding operation and singular value decomposition.
We learn that the first thing you need to do is determine a big problem to solve, or alternatively, a high-value benefit to provide.
Comparatively speaking, although the feedback controller law is established in the form of bilinear matrix inequality (BMI), it can be easily solved by alternatively fixing some parameters and optimizing the rest.
By substituting the forward blur model with any GAC model, we come to the GAC-based non-uniform deblurring method, which can be solved by alternatively updating pose weight matrix W and latent clear image x.
Similar to GoDec, the optimization problem of (3) can be solved by alternatively optimizing the following two subproblems until convergence: (5a) L t = arg min L ∗ X − L − S t − 1 F 2, (5b) S t = arg min card (S ) ≤ k X − L t − S F 2. In each iteration, we optimize the objective function by alternatively updating L and S. Firstly, the subproblem (5a) can be solved by [ 14].
Yet, we next show that it can be solved by alternatively solving the tractable "dual" per-antenna power balancing problems.
Here, we show that (16) can be solved by alternatively solving a sequence of the min-max power problems.
Alternatively, solving for m in terms of r, we can write H = d r (ln R /ln r) = d r log r R. It remains to minimize the number of cells N with respect to r, (4) ∂ H ∂ r = d ln R [ 1 ln r − (1 ln r ) 2 ] = 0 ⇒ ln r = 1.
