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Exact(3)
The following theorem shows that Algorithm 1 is global convergent under the conditions of Assumption A.
In this paper, we propose a new inexact line search rule for quasi-Newton method and establish some global convergent results of this method.
Moreover, for every, exists and is continuous on so that efficient gradient projection algorithms could be used to approximate the max-min power allocation for any power constraints if the algorithms were global convergent for some sufficiently large (as discussed before).
Similar(57)
First, we show that a delay nonlinear observer is globally convergent under the global Lipschitz condition of the system nonlinearity.
Now, we start our globally convergent method by using the global technique in Algorithm 3.1.
The second one is of global type, concerning the so-called globally convergent probability-one homotopy method.
Numerical experiments show that if then the gradient projection algorithm is not globally convergent, that is, it in general converges to a local maximum which is not global.
The global convergence and the (1+q -order convergent rate of the main algorithm are established under suitable conditions.
The global convergence and the (1+q -order convergent rate of the new method are established under suitable conditions.
This is a globally convergent method that does not require the assembly of the global matrix system and full Jacobian matrices.
Lemma 2 and the global convergence analysis in the next section show that the algorithm with search direction (d^{k}) is well defined and globally convergent. .
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com