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Stochastic steffensen-based conjugate gradient: A fast adaptive algorithmic framework for machine learning

delete2026-02-25
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杨壮 cover
杨壮 (Zhuang Yang)
DOI：10.1016/j.patcog.2026.113353delete
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Abstract

Abstract

En 中文
• A fast adaptive algorithmic framework with stochastic Steffensen-based conjugate gradient is proposed. • We theoretically analyze that the resulting algorithm attains an optimal oracle complexity. • Numerical experiments on different practical applications validate the effectiveness of the two proposed methods.
Keywords:
Stochastic Steffensen-based conjugate gradient
Adaptive algorithmic framework
Machine learning
Optimal oracle complexity
Numerical experiments

Journal

Pattern Recognition cover
Pattern Recognition
IF:
7.6
Papers:
1.3W
Citations:
4.5W

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