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In this paper, a projective splitting method for solving a class of generalized mixed variational inequalities is considered in Hilbert spaces.
We also discuss the convergence and stability of a new perturbed Ishikawa iterative algorithm for solving a class of generalized random nonlinear implicit quasi-variational inequalities involving random single-valued mappings.
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In this work, a new solution algorithm for globally solving a class of generalized fractional programming problems is presented.
This article presents a new approximation algorithm for globally solving a class of generalized fractional programming problems (P) whose objective functions are defined as an appropriate composition of ratios of affine functions.
In this paper, by extending the classical Newton method, we present the generalized Newton method (GNM) with high-order convergence for solving a class of large-scale linear complementarity problems, which is based on an additional parameter and a modulus-based nonlinear function.
In Section 2, we first consider a generalized Newton method (GNM) with high-order convergence rate for solving a class of the linear complementarity problems (1.1).
In view of this, by introducing a smooth equation and some reasonable equivalent reformulations, we investigate a generalized Newton iteration method with high-order convergence rate for solving a class of large-scale linear complementarity problem, which make full use of the superiority of the second-order convergence rate of the classical Newton method.
Mathematical programming is an art that provides precise techniques for solving a class of problems.
This algorithm was employed for solving a class of variable-order fractional differential equations.
Convergence analysis for the generalized Eckstein Bertsekas proximal point algorithm in the context of solving a class of nonlinear inclusion problems is explored.
The paper investigates the robust H∞ problem for a class of generalized forced Hamilton system with uncertainty.
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