SIAM Journal on Optimization· 2012Q1
Efficiency of Coordinate Descent Methods on Huge-Scale Optimization Problems
- 1,250citations
- Q1SCImago
- 2012year
Short summary
New coordinate descent methods using random partial updates significantly improve efficiency for huge-scale optimization problems, outperforming standard deterministic algorithms in certain cases.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduced coordinate descent methods with random partial updates for huge-scale optimization.
- Proved global convergence rate estimates for the new methods.
- Demonstrated superior performance compared to standard deterministic algorithms on certain objective functions.
- Validated high efficiency through numerical tests on very large problems.
AI-generated from the title and abstract; the full text is not read.
Abstract
In this paper we propose new methods for solving huge-scale optimization problems. For problems of this size, even the simplest full-dimensional vector operations are very expensive. Hence, we propose to apply an optimization technique based on random partial update of decision variables. For these methods, we prove the global estimates for the rate of convergence. Surprisingly, for certain classes of objective functions, our results are better than the standard worst-case bounds for deterministic algorithms. We present constrained and unconstrained versions of the method and its accelerated variant. Our numerical test confirms a high efficiency of this technique on problems of very big size.
The authors' abstract, as published at the source. SIAM Journal on Optimization, 2012 · DOI ↗
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Field: Numerical Analysis
Numerical AnalysisMathematics