Results 81 to 90 of about 2,178 (210)
AlgorithmsIn this paper, we propose an incremental-type subgradient scheme for solving a nonsmooth convex–concave minimax optimization problem in the setting of Euclidean spaces.
Thipagon Feesantia +2 more
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Fixed Point Theory and Applications, 2020 In this paper, we introduce a self-adaptive inertial subgradient extragradient method for solving pseudomonotone equilibrium problem and common fixed point problem in real Hilbert spaces.
Lateef Olakunle Jolaoso, Maggie Aphane
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Journal of Mathematics, Volume 2026, Issue 1, 2026.The goal of this study is dedicated to the research on the fixed point iterative algorithm for solving the common fixed point problems in Hilbert spaces. In this study, we first propose the hybrid and shrinking projection algorithms of two different nonexpansive mappings for a class of algorithms with two iterative sequences to solve the common fixed ...
Shengquan Weng +2 more
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Two-direction Subgradient Method for non-differentiable Optimization Problems
, 1981 One more search direction is introduced into the subgradient method for non-differentiable optimization problems to enhance the speed of convergence. The proposed algorithm generates a point which is strictly closer and forms a more acute angle to the ...
Kim, S, Lahn, HS, Koh, S
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Incremental Weak Subgradient Methods for Non-Smooth Non-Convex Optimization Problems
InformationNon-smooth, non-convex optimization problems frequently arise in modern machine learning applications, yet solving them efficiently remains a challenge.
Narges Araboljadidi, Valentina De Simone
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Journal of Inequalities and ApplicationsThis paper introduces an inertial subgradient-type algorithm for solving equilibrium problems with strong monotonicity, constrained over the fixed point set of a nonexpansive mapping in the framework of a real Hilbert space.
Manatchanok Khonchaliew, Narin Petrot
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Multi-Objective Optimization Design through Machine Learning for Drop-on-Demand Bioprinting
Engineering, 2019 Drop-on-demand (DOD) bioprinting has been widely used in tissue engineering due to its high-throughput efficiency and cost effectiveness. However, this type of bioprinting involves challenges such as satellite generation, too-large droplet generation ...
Jia Shi +3 more
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Linear Interpolation Method for Adversarial Attack [PDF]
Jisuanji kexueDeep neural networks exhibit significant vulnerability in the face of adversarial examples and are prone to attacks.The construction of adversarial examples can be abstracted as an optimization problem that maximizes the objective function.How-ever ...
CHEN Jun, ZHOU Qiang, BAO Lei, TAO Qing
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A subgradient projection method for quasiconvex minimization
PositivityAbstract In this paper, a subgradient projection method for quasiconvex minimization problems is provided. By using strong subdifferentials, it is proved that the generated sequence of the proposed algorithm converges to the solution of the minimization problem of a proper, lower semicontinuous and strongly quasiconvex function (in the sense of
Juan Choque +2 more
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Revisiting Subgradient Method: Complexity and Convergence Beyond Lipschitz Continuity
The subgradient method is one of the most fundamental algorithmic schemes for nonsmooth optimization. The existing complexity and convergence results for this method are mainly derived for Lipschitz continuous objective functions.
Zhu, Daoli +3 more
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