Suchergebnisse - Smoothing composite proximal gradient algorithm

Smoothing composite proximal gradient algorithm for sparse group Lasso problems with nonsmooth loss functions von Shen, Huiling, Peng, Dingtao, Zhang, Xian

“… Then, based on the smooth approximation of the loss function, a smoothing composite proximal gradient (SCPG …”
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Smoothing randomized block-coordinate proximal gradient algorithms for nonsmooth nonconvex composite optimization von Li, Xue, Bian, Wei

“… In this paper, we propose a smoothing randomized block-coordinate proximal gradient (S-RBCPG …”
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Proximal variable smoothing method for three-composite nonconvex nonsmooth minimization with a linear operator von Liu, Yuncheng, Xia, Fuquan

“… Based on the variable smoothing method, as well as first-order methods with suitable majorization techniques, we propose a proximal variable smoothing gradient (ProxVSG …”
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Zeroth-order Proximal Clipped Gradient Method with Shifts for Distributed Stochastic Composite Optimization Problems with Infinite Variance von Yang, Zhen-Ping, Chen, Pin-Bo, Zhao, Yong, Chen, Lin

“… -)gradient information may be unavailable. We present a mini-batch zeroth-order proximal clipped gradient algorithm with shifts, which utilizes the well-known Gaussian smoothing technique to yield unbiased zeroth-order gradient estimators …”
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Extrapolated Smoothing Descent Algorithm for Constrained Nonconvex and Nonsmooth Composite Problems von Chen, Yunmei, Liu, Hongcheng, Wang, Weina

“… Their algorithm adopts the proximal gradient algorithm with extrapolation and a safe-guarding policy to minimize the smoothed objective function for better practical and theoretical performance …”
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Adaptive smoothing algorithms for nonsmooth composite convex minimization von Tran-Dinh, Quoc

“… ” nonsmooth composite convex optimization problems. Our method combines both Nesterov’s accelerated proximal gradient scheme and a new homotopy strategy for smoothness parameter …”
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A Proximal Variable Smoothing for Nonsmooth Minimization Involving Weakly Convex Composite with MIMO Application von Kume, Keita, Yamada, Isao

“… We propose a proximal variable smoothing algorithm for nonsmooth optimization problem with sum of three functions involving weakly convex composite function …”
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Zeroth-order Proximal Clipped Gradient Method with Shifts for Distributed Stochastic Composite Optimization Problems with Infinite Variance: Zeroth-order Proximal Clipped Gradient Method with von Yang, Zhen-Ping, Chen, Pin-Bo, Zhao, Yong, Chen, Lin

“… -)gradient information may be unavailable. We present a mini-batch zeroth-order proximal clipped gradient algorithm with shifts, which utilizes the well-known Gaussian smoothing technique to yield unbiased zeroth-order gradient estimators …”
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A Stochastic Nesterov’s Smoothing Accelerated Method for General Nonsmooth Constrained Stochastic Composite Convex Optimization von Wang, Ruyu, Zhang, Chao, Wang, Lichun, Shao, Yuanhai

“… We propose a novel stochastic Nesterov’s smoothing accelerated method for general nonsmooth, constrained, stochastic composite convex optimization, the nonsmooth component of which may be not easy to compute its proximal operator …”
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Distributed Stochastic Frank-Wolfe for Constrained Composite Minimization von Hou, Jie, Zeng, Xianlin, Cui, Shisheng, Wang, Gang, Sun, Jian

“… However, existing distributed algorithms for composite minimization are designed exclusively for the unconstrained setting …”
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An accelerated non-Euclidean hybrid proximal extragradient-type algorithm for convex-concave saddle-point problems von Kolossoski, O., Monteiro, R.D.C.

“… The algorithm is a special instance of a non-Euclidean hybrid proximal extragradient framework introduced by Svaiter and Solodov …”
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An Inexact Interior-Point Lagrangian Decomposition Algorithm with Inexact Oracles von Liu, Deyi, Tran-Dinh, Quoc

“… We combine the Lagrangian dual decomposition, barrier smoothing, path-following, and proximal Newton techniques to develop a new inexact interior-point Lagrangian decomposition method to solve a broad …”
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A Proximal Variable Smoothing for Nonsmooth Minimization Involving Weakly Convex Composite with MIMO Application von Kume, Keita, Yamada, Isao

“… We propose a proximal variable smoothing algorithm for nonsmooth optimization problem with sum of three functions involving weakly convex composite function …”
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General Convergence Analysis of Stochastic First-Order Methods for Composite Optimization von Necoara, Ion

“… Based on the flexibility offered by our optimization model, we consider several variants of stochastic first-order methods, such as the stochastic proximal gradient and the stochastic proximal point algorithms …”
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A Decentralized Proximal Gradient Tracking Algorithm for Composite Optimization on Riemannian Manifolds von Wang, Lei, Le, Bao, Liu, Xin

“… })\). This paper proposes a proximal gradient type algorithm that fully exploits the composite structure …”
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Composite convex optimization with global and local inexact oracles von Sun, Tianxiao, Necoara, Ion, Tran-Dinh, Quoc

“… Such inexact oracles naturally arise in many situations, including primal–dual frameworks, barrier smoothing, and inexact evaluations of gradients and Hessians …”
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Adaptive Smoothing Algorithms for Nonsmooth Composite Convex Minimization von Tran-Dinh, Quoc

“… } for solving "fully" nonsmooth composite convex optimization problems. Our method combines both Nesterov's accelerated proximal gradient scheme and a new homotopy strategy for smoothness parameter …”
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A Zeroth-order Proximal Stochastic Gradient Method for Weakly Convex Stochastic Optimization von Pougkakiotis, Spyridon, Kalogerias, Dionysios S

“… The proposed algorithm utilizes the well-known Gaussian smoothing technique, which yields unbiased zeroth-order gradient estimators of a related partially smooth surrogate problem …”
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Hybrid Conditional Gradient - Smoothing Algorithms with Applications to Sparse and Low Rank Regularization von Argyriou, Andreas, Signoretto, Marco, Suykens, Johan

“… We study a hybrid conditional gradient - smoothing algorithm (HCGS) for solving composite convex optimization problems which contain several terms over a bounded set …”
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Efficient and Provable Algorithms for Convex Optimization Problems Beyond Lipschitz Continuous Gradients von Liu, Deyi

“… This thesis aims at developing efficient algorithms for solving complex and constrained convex optimization problems with provable convergence guarantees …”
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