.png){kind=icon}
返回
A Variance Reducing Stochastic Proximal Method with Acceleration Techniques
.png){kind=icon}
OA
AI
DOI：10.26599/TST.2022.9010051.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We consider a fundamental problem in the field of machine learning-structural risk minimization, which can be represented as the average of a large number of smooth component functions plus a simple and convex (but possibly non-smooth) function. In this paper, we propose a novel proximal variance reducing stochastic method building on the introduced Point-SAGA. Our method achieves two proximal operator calculations by combining the fast Douglas-Rachford splitting and refers to the scheme of the FISTA algorithm in the choice of momentum factors. We show that the objective function value converges to the iteration point at the rate of O(1/k) when each loss function is convex and smooth. In addition, we prove that our method achieves a linear convergence rate for strongly convex and smooth loss functions. Experiments demonstrate the effectiveness of the proposed algorithm, especially when the loss function is ill-conditioned with good acceleration.
Keyword:
composite optimization
Variance Reduction (VR)
fast Douglas-Rachford (DR) splitting
proximal operator
期刊
T
IF:
3.5
论文数:
987
被引数:
2.5K
机构
引用论文
Update: Multistate Outbreak of Monkeypox—Illinois, Indiana, Kansas, Missouri, Ohio, and Wisconsin, 2003.png)
JAMA
JAMAIF0
PREAI
分享收藏Improving Semantic Part Features for Person Re-identification with Supervised Non-local Similarity基于监督非局部相似性的人员再识别语义零件特征改进
OAAI
OAAI分享收藏WiFiHonk: Smartphone-Based Beacon Stuffed WiFi Car2X-Communication System for Vulnerable Road User SafetyWiFiHonk: 基于智能手机的信标填充WiFi Car2X-Communication系统，用于脆弱的道路使用者安全
PREAI
分享收藏An accelerated variance reducing stochastic method with Douglas-Rachford splittingMACHINE LEARNINGOAAI
MACHINE LEARNINGIF2.9
OAAI分享收藏 没有更多内容
没有更多内容