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To solve the convex optimization problem, the subgradient projection method based on dual decomposition is employed.
This paper presents a discriminative projection method based on Low-rank affinity matrix (LRA-DP) for robust feature extraction.
The method has been implemented in an incompressible Navier Stokes equations solver employing a projection method based on a staggered finite-volume algorithm on Cartesian grids.
Propagation of uncertainty from the model input parameters to the output stochastic variables is quantified using a non-intrusive spectral projection method based on polynomial chaos expansion.
As an example, we analyze the second-order projection method based on pressure increment formulations used by van Kan and Bell, Colella, and Glaz.
The purpose of this paper is to introduce a new hybrid projection method based on modified Mann iterative scheme by the generalized f-projection operator for a countable family of relatively quasi-nonexpansive mappings and the solutions of the system of generalized mixed equilibrium problems.
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A weighted Bregman-Gradient Projection denoising method, based on the Bregman iterative regularization (BIR) method and Chambolle's Gradient Projection method (or dual denoising method) is established.
We can also apply a method based on projection onto rings [13] to find an estimate of the target node position.
As we know, the fixed point method based on projection theory is a powerful tool to deal with complementary problems and variational inequalities in finite dimensional space [21].
Yang et al. propose a fast space-time adaptive processing (STAP) method based on projection approximation subspace tracking with sparse constraint [31].
In [29], a method based on projection onto hyperbolic sets was shown to perform better in this case; however, the robustness to NLOS is also lost.
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