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首页> 外文期刊>Abstract and applied analysis >Hybrid Extragradient Method with Regularization for Convex Minimization, Generalized Mixed Equilibrium, Variational Inequality and Fixed Point Problems
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Hybrid Extragradient Method with Regularization for Convex Minimization, Generalized Mixed Equilibrium, Variational Inequality and Fixed Point Problems

机译:带正则化的混合超梯度方法,用于凸极小化,广义混合均衡,变分不等式和不动点问题

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摘要

We introduce two iterative algorithms by the hybrid extragradient method with regularization for finding a common element of the set of solutions of the minimization problem for a convex and continuously Fréchet differentiable functional, the set of solutions of finite generalized mixed equilibrium problems, the set of solutions of finite variational inequalities for inverse strong monotone mappings and the set of fixed points of an asymptoticallyκ-strict pseudocontractive mapping in the intermediate sense in a real Hilbert space. We prove some strong and weak convergence theorems for the proposed iterative algorithms under mild conditions.
机译:我们介绍了两种混合正则化混合算法,用于寻找凸和连续Fréchet可微泛函最小化问题的解集,有限广义混合平衡问题的解集,解集实希尔伯特空间中的逆强单调映射的有限变分不等式和中间意义上的渐近κ严格伪压缩映射的不动点集。对于温和条件下的迭代算法,我们证明了一些强弱收敛定理。

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