mlsanityData to 7 Oct 2026

OCP-GN: A Scalable Second-order Optimizer for Stochastic Optimization

arXiv 2512.24552 ↗First posted 31 Dec 2025cs.CV math.OCcited by 0

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Abstract (arXiv)

This paper proposes a novel second-order optimization algorithm based on the Optimal Control Principle (OCP), applicable to large-scale optimization problems in neural network training. The algorithm has a computational complexity of O(d) and strong robustness. Extensive experiments on multiple benchmarks demonstrate the significant superiority of the proposed method.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 3 of its 7 references that are in the map. First posted on arXiv 31 Dec 2025.

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3 of 7 references.

References · 3

  1. 2017Geometric Loss Functions for Camera Pose Regression with Deep Learningcited by 224
  2. 2017Geometry-Aware Learning of Maps for Camera Localizationcited by 161
  3. 2025An Efficient Algorithm for Learning-Based Visual Localizationcited by 1

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