Donghua Wu

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Parallel iterative methods are powerful tool for solving large system of linear equations (LEs). The existing parallel computing research results are focussed mainly on sparse system or others with particular structure. And most are based on parallel implementation of the classical relaxation methods such as Gauss-Seidel, SOR, and AOR methods carried(More)
In this paper, we propose a new method for finding global optimum of continuous optimization problems, namely Level-Value Estimation algorithm(LVEM). First we define the variance function v(c) and the mean deviation function m(c) with respect to a single variable (the level value c), and both of these functions depend on the optimized function f (x). We(More)
—In this paper we propose an appealing inexact proximal alternating directions method (abbreviated as In-PADM) for solving a class of monotone variational inequalities with certain special structure, and this structure under consideration is common in practice. We prove convergence of In-PADM method while the inexact term is arbitrary but satisfied some(More)
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