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Algorithm (1.3) is found to be a gradient-projection method (GPM) in convex minimization.
A popular algorithm that solves SFP (1.1) is due to Byrne's CQ algorithm [2] which is found to be a gradient-projection method (GPM) in convex minimization.
A popular algorithm that solves the SFP (1.1) is due to Byrne's CQ algorithm [2], which is found to be a gradient-projection method (GPM) in convex minimization.
A seemingly more popular algorithm that solves the SFP is the CQ algorithm of Byrne [23, 27] which is found to be a gradient-projection method (GPM) in convex minimization.
A seemingly more popular algorithm that solves the SFP is the CQ algorithm of Byrne [19, 24] which is found to be a gradient-projection method (GPM) in convex minimization.
A more popular algorithm that solves the SFP seems to be the CQ algorithm of Byrne [2, 9] which is found to be a gradient-projection method (GPM) in convex minimization.
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Based on the viscosity approximation method, we use a gradient-projection algorithm to propose composite iterative algorithms and find a common solution of the problems which we studied.
For the retinotectal projection, there is a gradient across the retina in the number of Eph receptors on the growth cones of the retinal neurons.
Then we regularize it to find a unique solution by gradient-projection algorithm.
A special case of one of our iteration is modified gradient-projection algorithm.
The solution of the problem is found using Gradient Projection Method.
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