๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

An extragradient algorithm for solving general nonconvex variational inequalities

โœ Scribed by Muhammad Aslam Noor


Publisher
Elsevier Science
Year
2010
Tongue
English
Weight
251 KB
Volume
23
Category
Article
ISSN
0893-9659

No coin nor oath required. For personal study only.

โœฆ Synopsis


In this work, we suggest and analyze an extragradient method for solving general nonconvex variational inequalities using the technique of the projection operator. We prove that the convergence of the extragradient method requires only pseudomonotonicity, which is a weaker condition than requiring monotonicity. In this sense, our result can be viewed as an improvement and refinement of the previously known results. Our method of proof is very simple as compared with other techniques.


๐Ÿ“œ SIMILAR VOLUMES


Modified extragradient methods for solvi
โœ Abdellah Bnouhachem; M.H. Xu; Xiao-Ling Fu; Sheng Zhaohan ๐Ÿ“‚ Article ๐Ÿ“… 2009 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 535 KB

In this paper, we propose two methods for solving variational inequalities. In the first method, we modified the extragradient method by using a new step size while the second method can be viewed as an extension of the first one by performing an additional projection step at each iteration and anot

Existence and algorithm for solving some
โœ Xie Ping Ding; Chun Lin Luo ๐Ÿ“‚ Article ๐Ÿ“… 1999 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 427 KB

In this paper, by applying the auxiliary variational principle technique, an existence theorem of solutions for a new class of generalized mixed variational inequalities is proved in Hilbert spaces. A novel and innovative iterative algorithm to compute approximate solutions is suggested and analyzed

A predictor-corrector algorithm for gene
โœ M.A. Noor ๐Ÿ“‚ Article ๐Ÿ“… 2001 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 368 KB

In this paper, we suggest a new predictor-corrector algorithm for solving general variational inequalities by using the auxiliary principle technique. The convergence of the proposed method only requires the partially relaxed strong monotonicity of the operator, which is weaker than co-coercivity. A