A variable-metric non-monotone line search method based on the Fukushima regularized gap function is introduced for mixed variational inequalities and equilibrium problems, with global convergence and R-linear rate proved under strong monotonicity.
A Scaled Gradient Modified Non-monotone Line Search Method for Constrained Optimization Problems
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abstract
In this paper, we propose a scaled gradient modified non-monotone line search method for solving constrained minimization problems, and explore several specific properties of this method, namely, its convergence analysis. We discuss the linear convergence rate of the sequence generated by the proposed algorithm to a solution of the constrained minimization problem where the objective function is strongly quasiconvex. We consider numerical examples of large-scale fractional programming and quadratic programming for the function of pseudo convex and strongly quasiconvex and compare the performance of the proposed algorithm with the existing ones for these examples.
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math.OC 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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A Variable-Metric Non-monotone Line Search Method for Mixed Variational Inequalities and Equilibrium Problems
A variable-metric non-monotone line search method based on the Fukushima regularized gap function is introduced for mixed variational inequalities and equilibrium problems, with global convergence and R-linear rate proved under strong monotonicity.