Results 31 to 40 of about 39,060 (206)
Stochastic Frank-Wolfe Methods for Nonconvex Optimization
, 2016 We study Frank-Wolfe methods for nonconvex stochastic and finite-sum optimization problems. Frank-Wolfe methods (in the convex case) have gained tremendous recent interest in machine learning and optimization communities due to their projection-free ...
Poczos, Barnabas +3 more
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Stochastic Optimal Growth with Nonconvexities
Journal of Mathematical Economics, 2006 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nishimura, Kazuo +2 more
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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
doaj +1 more source
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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, 2014 Yuan's theorem of the alternative is an important theoretical tool in optimization, which provides a checkable certificate for the infeasibility of a strict inequality system involving two homogeneous quadratic functions.
Hu, Shenglong, Li, Guoyin, Qi, Liqun
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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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Deterministic Nonsmooth Nonconvex Optimization
, 2023 We study the complexity of optimizing nonsmooth nonconvex Lipschitz functions by producing $(δ,ε)$-stationary points. Several recent works have presented randomized algorithms that produce such points using $\tilde O(δ^{-1}ε^{-3})$ first-order oracle calls, independent of the dimension $d$. It has been an open problem as to whether a similar result can
Jordan, Michael I. +4 more
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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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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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Data‐Driven Bulldozer Blade Control for Autonomous Terrain Leveling
Advanced Robotics Research, EarlyView.A simulation‐driven framework for autonomous bulldozer leveling is presented, combining high‐fidelity terramechanics simulation with a neural‐network‐based reduced‐order model. Gradient‐based optimization enables efficient, low‐level blade control that balances leveling quality and operation time.
Harry Zhang +5 more
wiley +1 more source