Results 11 to 20 of about 6,996 (240)
Regularized gap function as penalty term for constrained minimization problems [PDF]
Journal of Mathematical Analysis and Applications, 2009 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chandal Nahak
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Regularized gap functions for nonsmooth variational inequality problems [PDF]
Journal of Mathematical Analysis and Applications, 2007 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Tan, Lu Lin
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Error Bounds of Regularized Gap Functions for Polynomial Variational Inequalities [PDF]
Journal of Optimization Theory and Applications, 2021 This paper is devoted to present new error bounds of regularized gap functions for polynomial variational inequalities with exponents explicitly determined by the dimension of the underlying space and the number/degree of the involved polynomials. The developed techniques are largely based on variational analysis and semialgebraic geometry, which allow
Tiến-Sơn Pham +2 more
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Error bounds of regularized gap functions for nonmonotone Ky Fan inequalities
Journal of Industrial and Management Optimization, 2020 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Minghua Li
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A REMARK ON THE REGULARIZED GAP FUNCTION FOR IQVI
Journal of the Chungcheng Mathematical Society, 2015 Abstract. Aussel et al. [1] introduced the notion of inverse quasi-variational inequalities (IQVI) by combining quasi-variational in-equalities and inverse variational inequalities. Discussions are madein a flnite dimensional Euclidean space. In this note, we developan inflnite dimensional version of IQVI by investigating some ba-sic properties of the ...
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Mathematical Control and Related Fields, 2014 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Haisen Zhang
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Overcoming Lower-Level Constraints in Bilevel Optimization: A Novel Approach with Regularized Gap Functions [PDF]
Constrained bilevel optimization tackles nested structures present in constrained learning tasks like constrained meta-learning, adversarial learning, and distributed bilevel optimization. However, existing bilevel optimization methods mostly are typically restricted to specific constraint settings, such as linear lower-level constraints. In this work,
Wei Yao +3 more
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Bridging the Gap Between Target Networks and Functional Regularization
CoRR, 2021 Bootstrapping is behind much of the successes of deep Reinforcement Learning. However, learning the value function via bootstrapping often leads to unstable training due to fast-changing target values. Target Networks are employed to stabilize training by using an additional set of lagging parameters to estimate the target values.
Alexandre Piché +6 more
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A NOTE ON A REGULARIZED GAP FUNCTION OF QVI IN BANACH SPACES
Journal of the Chungcheng Mathematical Society, 2014 exaly +3 more sources
Convergence Rate Analysis of a Class of Uncertain Variational Inequalities
IEEE Access, 2023 Variational inequalities theory is a potent tool that can be employed to tackle diverse optimization problems, with applications spanning physics, economics, finance and beyond.
Cunlin Li, Teng Zhao, Hooi Min Yee
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