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Then, we recast the signal recovery problem as a smoothing penalized least squares optimization problem, and apply the nonlinear conjugate gradient method to solve the smoothing

Then, we recast the signal recovery problem as a smoothing penalized least squares optimization problem, and apply the nonlinear conjugate gradient method to solve the smoothing

The purpose of this talk is to analyze new hybrid proximal point algorithms and solve the constrained minimization problem involving a convex functional in a uni- formly convex

where L is lower triangular and U is upper triangular, then the operation counts can be reduced to O(2n 2 )!.. The results are shown in the following table... 113) in

This kind of algorithm has also been a powerful tool for solving many other optimization problems, including symmetric cone complementarity problems [15, 16, 20–22], symmetric

To improve the convergence of difference methods, one way is selected difference-equations in such that their local truncation errors are O(h p ) for as large a value of p as

Methods involving finite differences for solving boundary-value problems replace each of the derivatives in the differential equation by an appropriate

In order to solve the problems mentioned above, the following chapters intend to make a study of the structure and system of The Significance of Kuangyin Sūtra, then to have