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Learning Provably Improves the Convergence of Gradient Descent

arXiv — cs.LG•Wednesday, October 29, 2025 at 4:00:00 AM
PositiveArtificial Intelligence
A recent study has shown that learning can significantly enhance the convergence of gradient descent methods in optimization tasks. The research addresses a critical gap in the existing literature, which often relies on unrealistic assumptions about training convergence. By providing a rigorous theoretical foundation for the Learn to Optimize (L2O) framework, this work not only validates the effectiveness of L2O in solving both convex and non-convex problems but also opens new avenues for improving optimization techniques in various applications. This advancement is crucial for fields that rely on efficient algorithm performance.
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