Results 21 to 30 of about 1,804,722 (202)
IEEE Access, 2018 In the past decade, sparse and low-rank recovery has drawn much attention in many areas such as signal/image processing, statistics, bioinformatics, and machine learning.
Fei Wen +3 more
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Nonconvex Sparse Representation With Slowly Vanishing Gradient Regularizers
IEEE Access, 2020 Sparse representation has been widely used over the past decade in computer vision and signal processing to model a wide range of natural phenomena.
Eunwoo Kim, Minsik Lee, Songhwai Oh
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Nonconvex Low Tubal Rank Tensor Minimization
IEEE Access, 2019 In the sparse vector recovery problem, the L0-norm can be approximated by a convex function or a nonconvex function to achieve sparse solutions. In the low-rank matrix recovery problem, the nonconvex matrix rank can be replaced by a convex function or a ...
Yaru Su, Xiaohui Wu, Genggeng Liu
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A trust region method for finding second-order stationarity in linearly constrained nonconvex optimization [PDF]
, 2020 Motivated by the TRACE algorithm [F. E. Curtis, D. P. Robinson, and M. Samadi, Math. Program., 162 (2017), pp. 1-32], we propose a trust region algorithm for finding second-order stationary points of a linearly constrained nonconvex optimization problem.
Nouiehed, Maher, Razaviyayn, Meisam
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Journal of the Mechanical Behavior of Materials, 2015 This paper introduces constructing convex-relaxed programs for nonconvex optimization problems. Branch-and-bound algorithms are convex-relaxation-based techniques.
Keller André A.
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Vector optimization problems with nonconvex preferences
, 2008 In this paper, some vector optimization problems are considered where pseudo-ordering relations are determined by nonconvex cones in Banach spaces. We give some characterizations of solution sets for vector complementarity problems and vector variational
Rubinov, AM +5 more
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Linearized ADMM for Nonconvex Nonsmooth Optimization With Convergence Analysis
IEEE Access, 2019 Linearized alternating direction method of multipliers (ADMM) as an extension of ADMM has been widely used to solve linearly constrained problems in signal processing, machine learning, communications, and many other fields.
Qinghua Liu, Xinyue Shen, Yuantao Gu
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Journal of MathematicsThis paper defines a strong convertible nonconvex (SCN) function for solving the unconstrained optimization problems with the nonconvex or nonsmooth (nondifferentiable) function.
Min Jiang +3 more
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Positive definite estimation of large covariance matrix using generalized nonconvex penalties
IEEE Access, 2016 This paper addresses the issue of large covariance matrix estimation in a high-dimensional statistical analysis. Recently, improved iterative algorithms with positive-definite guarantee have been developed.
Fei Wen +3 more
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Advanced Functional Materials, EarlyView.Here, we develop a high‐throughput algorithm that accelerates the elucidation of phase diagrams of topological spin textures using limited computational resources at high numerical accuracy. Applying this framework to the van der Waals magnet CrSBr, we unveiled a hierarchy of previously unknown topological textures (domain‐wall bimerons, bimeron chains,
Andrew Lyall +4 more
wiley +1 more source