Gradient Descent Outperforms Ridge Regression in Linear Models

A new research paper published on arXiv analyzes the performance of gradient descent (GD) compared to ridge regression and online stochastic gradient descent (SGD) in linear regression tasks. The study finds that GD consistently outperforms ridge regression, offering comparable or superior risk profiles. However, GD and SGD show incomparable performance, with each algorithm excelling in different problem types. AI

IMPACT Provides theoretical insights into optimization algorithms relevant to machine learning model training.

RANK_REASON Academic paper detailing theoretical analysis of algorithms. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• arXiv
• Gradient Descent
• Jingfeng Wu
• Stochastic Gradient Descent
• Ridge Regression

• paper
• other

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