Sentence examples for minimization algorithm to from inspiring English sources

In this paper we propose an accelerated reweighted nuclear norm minimization algorithm to recover a low rank matrix.

To this end, a methodology based on model simulations is used that employs three statistical methods: (i) Monte-Carlo procedure: to find proper initial conditions, (ii) Identifiability analysis: to find an identifiable parameter subset of the fuzzy controller and (iii) minimization algorithm: to fine-tune the identifiable parameter subset of the controller.

We have also developed and analyzed the convergence of an alternating minimization algorithm to solve the resulting nonsmooth-nonconvex regularization problem.

In Fig. 7e, the method is proposed based on the nonlocal TV and uses a linearized proximal alternating minimization algorithm to improve the efficiency.

Figure 13 shows the four Gaussians which were found by the Expected Minimization algorithm to specify the density in the first three dimensions and the two trajectories of the solutions found by the two AAM during the convergence.

Finally, under the guidelines of defined metrics, the sizing optimization problem is formulated, and then we propose the capital expenditure (CAPEX) minimization algorithm to resolve it with considerations of communication reliability, efficiency, and durability.

Some optimization algorithms have been designed as the local minimization algorithms to overcome this default [13 17].

A third approach has used error minimization algorithms to predict scale structures under the assumption of competing preferences for small integer ratios and equal intervals between successive scale tones [15], [16].

In order to have a picture of how this landscape looks like, we have applied our frustration minimization algorithms to uniformly distributed initial conditions and registered the local and global minima achieved in the process (see Fig. 3 and Table S3).

We present an efficient recursive algorithm, termed Matching-Minimization algorithm, to compute the sample mean of a set of spike trains.

In this work, we investigate and demonstrate the application of the TV-minimization algorithm to reconstructing images from mouse data acquired with a CNT-based CT scanner at a number of views much lower than what is used in conventional CT imaging.