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Limited-memory Common-directions Method For Distributed Optimization And Its Application On Empirical Risk Minimization.pdf

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Preview of Limited-memory common-directions method for distributed optimization and its application on empirical risk minimization
🔗 Source: csie.ntu.edu.tw
📊 Size: 1.27 MB
📄 Pages: 10 pages
⬇️ Downloads: 43

Summary

Distributed optimization is crucial for handling large volumes of data, but it faces challenges due to inter-machine communication costs. The common-directions method offers fast convergence but has high spatial and computational costs. To address this, a limited-memory common-directions method is proposed, which reduces memory consumption and computational costs while maintaining fast convergence. This method is suitable for large-scale distributed optimization and is applied to empirical risk minimization (ERM) problems, outperforming state-of-the-art distributed optimization methods.

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Distributed optimization is crucial for handling large volumes of data, but it faces challenges due to inter-machine communication costs.

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  • File Format: PDF
  • File Size: 1.27 MB
  • Pages: 10
  • Language: EN
  • Total Downloads: 43
  • Last Updated: 2 hours ago

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Limited-memory guideLimited-memory tutorialcommon-directions guidecommon-directions tutorialmethod guidemethod tutorialdistributed guidedistributed tutorialoptimization guideoptimization tutorialapplication guideapplication tutorialempirical guideempirical tutorialrisk guiderisk tutorialminimization guideminimization tutorial

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