Arc Gradient Descent: A Geometrically Motivated Gradient Descent-based Optimiser with Phase-Aware, User-Controlled Step Dynamics (proof-of-concept)
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Arc Gradient Descent is a new optimizer with phase-aware, user-controlled step dynamics for improved convergence
Action Steps
- Formulate the optimization problem using the ArcGD optimizer
- Implement the ArcGD optimizer on a non-convex benchmark function, such as the Rosenbrock function
- Evaluate the performance of ArcGD compared to existing optimizers, like Adam
- Apply the ArcGD optimizer to real-world ML datasets to assess its effectiveness
Who Needs to Know This
Machine learning researchers and engineers can benefit from this optimizer, as it provides more control over the optimization process and potentially leads to better convergence on complex problems
Key Insight
💡 The ArcGD optimizer offers user-controlled step dynamics, which can lead to improved convergence on complex, non-convex problems
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🚀 Introducing ArcGD: a geometrically motivated optimizer with phase-aware step dynamics! 💡
Key Takeaways
Arc Gradient Descent is a new optimizer with phase-aware, user-controlled step dynamics for improved convergence
Full Article
Title: Arc Gradient Descent: A Geometrically Motivated Gradient Descent-based Optimiser with Phase-Aware, User-Controlled Step Dynamics (proof-of-concept)
Abstract:
arXiv:2512.06737v3 Announce Type: replace-cross Abstract: The paper presents the formulation, implementation, and evaluation of the ArcGD optimiser. The evaluation is conducted initially on a non-convex benchmark function and subsequently on a real-world ML dataset. The initial comparative study using the Adam optimiser is conducted on a stochastic variant of the highly non-convex and notoriously challenging Rosenbrock function, renowned for its narrow, curved valley, across dimensions ranging f
Abstract:
arXiv:2512.06737v3 Announce Type: replace-cross Abstract: The paper presents the formulation, implementation, and evaluation of the ArcGD optimiser. The evaluation is conducted initially on a non-convex benchmark function and subsequently on a real-world ML dataset. The initial comparative study using the Adam optimiser is conducted on a stochastic variant of the highly non-convex and notoriously challenging Rosenbrock function, renowned for its narrow, curved valley, across dimensions ranging f
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