mlsanityData to 7 Oct 2026

Coordinate Descent Algorithms

arXiv 1502.04759 ↗First posted 17 Feb 2015math.OCcited by 218

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Abstract (arXiv)

Coordinate descent algorithms solve optimization problems by successively performing approximate minimization along coordinate directions or coordinate hyperplanes. They have been used in applications for many years, and their popularity continues to grow because of their usefulness in data analysis, machine learning, and other areas of current interest. This paper describes the fundamentals of the coordinate descent approach, together with variants and extensions and their convergence properties, mostly with reference to convex objectives. We pay particular attention to a certain problem structure that arises frequently in machine learning applications, showing that efficient implementations of accelerated coordinate descent algorithms are possible for problems of this type. We also present some parallel variants and discuss their convergence properties under several models of parallel execution.

Key facts. Cited by 218 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Feb 2015.

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Citing papers per year: 2021: 39; 2022: 36; 2023: 36; 2024: 41; 2025: 34; 2026: 32.

218 papers.

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  10. 2023Convergence Rates for Stochastic Approximation: Biased Noise with Unbounded Variance, and Applicationscited by 15
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