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New Two-Sided L-BFGS algorithm enhances optimization stability

By PulseAugur Editorial · [3 sources] ·

Researchers have developed a new variant of the limited-memory BFGS (L-BFGS) optimization algorithm, called Two-Sided L-BFGS. This method addresses the issue of exploding condition numbers in the inverse Hessian approximation, which can cause numerical instability in complex optimization landscapes. The Two-Sided L-BFGS algorithm uses a geometric envelope to dynamically constrain the condition number, ensuring stability while preserving computational efficiency and global convergence properties. AI

IMPACT Improves numerical stability and convergence for large-scale optimization problems, potentially benefiting AI model training.

RANK_REASON The cluster contains an academic paper detailing a new algorithm and its theoretical properties.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New Two-Sided L-BFGS algorithm enhances optimization stability

COVERAGE [3]

  1. arXiv cs.LG TIER_1 English(EN) · Don Li ·

    On the Condition Number Upper Bound of the L-BFGS Inverse Hessian Approximation Matrix with a Two-Sided Geometric Envelope Safeguarding Mechanism

    arXiv:2607.05836v1 Announce Type: cross Abstract: The limited-memory BFGS (L-BFGS) algorithm is a cornerstone of large-scale optimization due to its linear memory and computational costs. However, in ill-conditioned or non-convex landscapes, the implicit inverse Hessian approxima…

  2. arXiv cs.LG TIER_1 English(EN) · Don Li ·

    On the Condition Number Upper Bound of the L-BFGS Inverse Hessian Approximation Matrix with a Two-Sided Geometric Envelope Safeguarding Mechanism

    The limited-memory BFGS (L-BFGS) algorithm is a cornerstone of large-scale optimization due to its linear memory and computational costs. However, in ill-conditioned or non-convex landscapes, the implicit inverse Hessian approximation can suffer from an exploding condition number…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    On the Condition Number Upper Bound of the L-BFGS Inverse Hessian Approximation Matrix with a Two-Sided Geometric Envelope Safeguarding Mechanism

    The limited-memory BFGS (L-BFGS) algorithm is a cornerstone of large-scale optimization due to its linear memory and computational costs. However, in ill-conditioned or non-convex landscapes, the implicit inverse Hessian approximation can suffer from an exploding condition number…

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