A Line Search Based Proximal Stochastic Gradient Algorithm with Dynamical Variance Reduction (vol 94, 23, 2023)

被引:0
|
作者
Franchini, Giorgia [1 ]
Porta, Federica [1 ]
Ruggiero, Valeria [2 ]
Trombini, Ilaria [2 ,3 ]
机构
[1] Univ Modena & Reggio Emilia, Dept Phys Informat & Math, Via Campi 213-B, I-41125 Modena, Italy
[2] Univ Ferrara, Dept Math & Comp Sci, Via Machiavelli 30, I-44121 Ferrara, Italy
[3] Univ Parma, Dept Math Phys & Comp Sci, Parco Area Sci 7-A, I-43124 Parma, Italy
关键词
D O I
10.1007/s10915-023-02267-6
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
This file contains a revised version of the proofs of Theorems 3 and 4 of the paper [1]. In particular, a more correct argument is employed to obtain the inequality (A11) from (A10), provided that a stronger hypothesis on the sequence {epsilon(k)} is included. The practical implementation of the algorithm (Section 3) remains as it is and all the numerical experiments (Section 4) are still valid since the stronger hypothesis on {epsilon(k)} was already satisfied by the selected setting of the hyperparameters.
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页数:6
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