SGDLibrary: a MATLAB library for stochastic optimization algorithms. We consider the problem of finding the minimizer of a function f:ℝ d →ℝ of the finite-sum form minf(w)=1/n∑ i n f i (w). This problem has been studied intensively in recent years in the field of machine learning (ML). One promising approach for large-scale data is to use a stochastic optimization algorithm to solve the problem. SGDLibrary is a readable, flexible and extensible pure-MATLAB library of a collection of stochastic optimization algorithms. The purpose of the library is to provide researchers and implementers a comprehensive evaluation environment for the use of these algorithms on various ML problems.
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References in zbMATH (referenced in 6 articles , 1 standard article )
Showing results 1 to 6 of 6.
- He, Fang; Wang, Xiao; Chen, Xiaojun: A penalty relaxation method for image processing using Euler’s elastica model (2021)
- An, Xibin; He, Bing; Hu, Chen; Liu, Bingqi: Online supervised learning with distributed features over multiagent system (2020)
- Pan, Yuangang; Tsang, Ivor W.; Singh, Avinash K.; Lin, Chin-Teng; Sugiyama, Masashi: Stochastic multichannel ranking with brain dynamics preferences (2020)
- Yuan, Xiao-Tong; Li, Ping: On convergence of distributed approximate Newton methods: globalization, sharper bounds and beyond (2020)
- Vinod Kumar Chauhan, Anuj Sharma, Kalpana Dahiya: LIBS2ML: A Library for Scalable Second Order Machine Learning Algorithms (2019) arXiv
- Kasai, Hiroyuki: SGDLibrary: a MATLAB library for stochastic optimization algorithms (2018)