Extensions of Korpelevich's extragradient method for the variational inequality problem in Euclidean space
By Yair Censor, Aviv Gibali, Simeon Reich. 2012
439 citations recorded by the source corpus.
Subfields: Applied Mathematics, Computational Theory and Mathematics, Numerical Analysis
View Paper: https://www.semanticscholar.org/paper/794ddb3ddb289a11b2bf51451df5e2cf515104d8
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In Applied Mathematics
- Duong Viet Thong
- Oluwatosin Temitope Mewomo
- Phan Tu Vuong
- Rapeepan Kraikaew
- Satit Saejung
- Timilehin Opeyemi Alakoya
- Aviv Gibali
- Grace Nnennaya Ogwo
In Computational Theory and Mathematics
- Oluwatosin Temitope Mewomo
- Duong Viet Thong
- Aviv Gibali
- Timilehin Opeyemi Alakoya
- Satit Saejung
- Rapeepan Kraikaew
- Lateef Olakunle Jolaoso
- Adeolu Taiwo
In Numerical Analysis
- Oluwatosin Temitope Mewomo
- Duong Viet Thong
- Aviv Gibali
- Timilehin Opeyemi Alakoya
- Lateef Olakunle Jolaoso
- Satit Saejung
- Rapeepan Kraikaew
- Adeolu Taiwo
Citations in the corpus
- Fast and Simple Bregman Projection Methods for Solving Variational Inequalities and Related Problems in Banach Spaces (2020, background): “There exist many generalizations/modifications of the SEM which try to tackle these issues, for example, see [8,9,20,23]).”
- Strong Convergence of the Halpern Subgradient Extragradient Method for Solving Variational Inequalities in Hilbert Spaces (2014, methodology): “The same authors continued the study of this method in [15, 16].”
- Modified Tseng's extragradient methods for solving pseudo-monotone variational inequalities (2019, methodology): “This method converges for pseudo-monotone VIs in finite dimensional Euclidean spaces [8] and monotone VIs in infinite dimensional Hilbert spaces [6,7].”
- Iterative algorithm with self-adaptive step size for approximating the common solution of variational inequality and fixed point problems (2021, positive): “Censor et al [11]modified Algorithm (2) by replacing the second projection onto C by a projection onto a specific constructible half-space and called it the subgradient extragradient method which is defined as follows: ⎪⎪⎪⎪⎨ ⎪⎪⎪⎪⎩ x1 ∈ H, yn = PC(xn − λnAxn), Tn := {w ∈ H : 〈xn − λnAxn − yn,w − yn〉 ”