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New PyTorch package accelerates large-scale optimization with randomized linear algebra

By PulseAugur Editorial · [1 sources] ·

A new PyTorch-based package called rlaopt has been released, designed to accelerate large-scale optimization and scientific computing using randomized numerical linear algebra (RandNLA). This package offers GPU-enabled solvers for various problems, including linear systems and empirical risk minimization, utilizing methods like NystromPCG, NysADMM, and SAPPHIRE. rlaopt also features a modeling language for intuitive problem specification and supports differentiation through its iterations for applications such as hyperparameter tuning. Experiments have demonstrated significant speedups from GPU execution and identified scenarios where randomized preconditioning enhances performance. AI

IMPACT This package could accelerate research and development in machine learning by providing efficient tools for large-scale optimization problems.

RANK_REASON The item is a research paper detailing a new software package for scientific computing. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New PyTorch package accelerates large-scale optimization with randomized linear algebra

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The item is a research paper detailing a new software package for scientific computing. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Pratik Rathore, Zachary Frangella, Parth Nobel, Xuning Hu, Madeleine Udell ·

    GPU-Enabled Large-Scale Optimization Using Randomized Linear Algebra

    arXiv:2609.08136v1 Announce Type: new Abstract: This paper introduces rlaopt, a PyTorch-based package for large-scale optimization and scientific computing using randomized numerical linear algebra (RandNLA). Despite substantial progress in RandNLA-based algorithms, few implement…

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