Results 81 to 90 of about 19,600 (195)
Bias-Reduction in Variational Regularization
, 2016 The aim of this paper is to introduce and study a two-step debiasing method for variational regularization. After solving the standard variational problem, the key idea is to add a consecutive debiasing step minimizing the data fidelity on an appropriate
Brinkmann, Eva-Maria +3 more
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``Efficient” Subgradient Methods for General Convex Optimization [PDF]
SIAM Journal on Optimization, 2016 A subgradient method is presented for solving general convex optimization problems, the main requirement being that a strictly-feasible point is known. A feasible sequence of iterates is generated, which converges to within user-specified error of optimality.
openaire +3 more sources
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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Tropical analysis: With an application to indivisible goods
Theoretical Economics, Volume 20, Issue 3, Page 815-829, July 2025.We establish the subgradient theorem for monotone correspondences: a monotone correspondence is equal to the subdifferential of a potential if and only if it is conservative, i.e., its integral along a closed path vanishes irrespective of the selection from the correspondence along the path.
Nicholas C. Bedard, Jacob K. Goeree
wiley +1 more source
Robust Reduced-Rank Adaptive Processing Based on Parallel Subgradient Projection and Krylov Subspace Techniques [PDF]
, 2013 In this paper, we propose a novel reduced-rank adaptive filtering algorithm by blending the idea of the Krylov subspace methods with the set-theoretic adaptive filtering framework.
Isao Yamada +3 more
core
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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Scaling Techniques for ε-Subgradient Methods.
SIAM J. Optim., 2016 The recent literature on first order methods for smooth optimization shows that significant improvements on the practical convergence behavior can be achieved with variable step size and scaling for the gradient, making this class of algorithms attractive for a variety of relevant applications.
Silvia Bonettini +2 more
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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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, 2012 In this note, we present a new averaging technique for the projected stochastic subgradient method. By using a weighted average with a weight of t+1 for each iterate w_t at iteration t, we obtain the convergence rate of O(1/t) with both an easy proof and
Bach, Francis +2 more
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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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