Suchergebnisse - Surrogate sub-gradient algorithm

Supply chain networks design with multi-mode demand satisfaction policy von Ardalan, Zaniar, Karimi, Sajad, Naderi, B., Arshadi Khamseh, Alireza

“… •This paper deals with a supply chain network design with multi-mode demand.•The problem is mathematically formulated as mixed integer linear programming.•A …”
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Parsimonious shooting heuristic for trajectory design of connected automated traffic part II: Computational issues and optimization von Ma, Jiaqi, Li, Xiaopeng, Zhou, Fang, Hu, Jia, Park, B. Brian

“… ) proposed a parsimonious shooting heuristic (SH) algorithm for constructing feasible trajectories for a stream of vehicles considering realistic constraints including …”
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A new envelope function for nonsmooth DC optimization von Themelis, Andreas, Hermans, Ben, Patrinos, Panagiotis

“… ". A gradient method on this surrogate function yields a novel (sub)gradient-free proximal algorithm which is inherently parallelizable and can handle fully nonsmooth formulations …”
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A Fast and Efficient Data Association of Passive Sensor Tracking von Changning Tong, Yuesong Lin, Yunfei Guo, Yan Zuo

“… The sub gradient is applied to update the Lagrange multipliers, but it needs to minimize all the sub problems at every iterative time to solve the dual solution in the classic algorithm …”
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A new envelope function for nonsmooth DC optimization von Themelis, Andreas, Hermans, Ben, Patrinos, Panagiotis

“… ". A gradient method on this surrogate function yields a novel (sub)gradient-free proximal algorithm which is inherently parallelizable and can handle fully nonsmooth formulations …”
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Large-margin classification with multiple decision rules von Kimes, Patrick K., Hayes, David Neil, Marron, J. S., Liu, Yufeng

“… Binary classification is a common statistical learning problem in which a model is estimated on a set of covariates for some outcome, indicating the membership …”
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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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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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Large-Margin Classification with Multiple Decision Rules von Kimes, Patrick K, Hayes, D Neil, Marron, J S, Liu, Yufeng

“… Binary classification is a common statistical learning problem in which a model is estimated on a set of covariates for some outcome indicating the membership …”
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Optimization methods for regularized convex formulations in machine learning von Lee, Sang Kyun

“… We develop efficient numerical optimization algorithms for regularized convex formulations that appear in a variety of areas such as machine learning, statistics, and signal processing …”
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A Zeroth-order Proximal Stochastic Gradient Method for Weakly Convex Stochastic Optimization von Pougkakiotis, Spyridon, Kalogerias, Dionysios S

“… We consider nonsmooth and nonlinear stochastic composite problems, for which (sub-)gradient information might be unavailable …”
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