Imagen de Portada

The Method of Infinite Descent

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Batley, Reza T., Saha, Sourav
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Optimization and Control
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Armijo Line-search Can Make (Stochastic) Gradient Descent Provably Faster
por: Vaswani, Sharan, et al.
Publicado: (2025)

Convergence of Steepest Descent and Adam under Non-Uniform Smoothness
por: Vaswani, Sharan, et al.
Publicado: (2026)

Implicit Bias and Convergence of Matrix Stochastic Mirror Descent
por: Akhtiamov, Danil, et al.
Publicado: (2026)

Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent
por: Vaswani, Sharan, et al.
Publicado: (2021)

On Penalty-based Bilevel Gradient Descent Method
por: Shen, Han, et al.
Publicado: (2023)

Stochastic Adaptive Gradient Descent Without Descent
por: Aujol, Jean-François, et al.
Publicado: (2025)

A Mirror Descent Perspective of Smoothed Sign Descent
por: Wang, Shuyang, et al.
Publicado: (2024)

First and Second Order Approximations to Stochastic Gradient Descent Methods with Momentum Terms
por: Lu, Eric
Publicado: (2025)

DoWG Unleashed: An Efficient Universal Parameter-Free Gradient Descent Method
por: Khaled, Ahmed, et al.
Publicado: (2023)

Preconditioned Norms: A Unified Framework for Steepest Descent, Quasi-Newton and Adaptive Methods
por: Veprikov, Andrey, et al.
Publicado: (2025)

Corner Gradient Descent
por: Yarotsky, Dmitry
Publicado: (2025)

Random Function Descent
por: Benning, Felix, et al.
Publicado: (2023)

Robustness of Iteratively Pre-Conditioned Gradient-Descent Method: The Case of Distributed Linear Regression Problem
por: Chakrabarti, Kushal, et al.
Publicado: (2021)

Iterative Pre-Conditioning for Expediting the Gradient-Descent Method: The Distributed Linear Least-Squares Problem
por: Chakrabarti, Kushal, et al.
Publicado: (2020)

A Local Polyak-Lojasiewicz and Descent Lemma of Gradient Descent For Overparametrized Linear Models
por: Xu, Ziqing, et al.
Publicado: (2025)

Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates
por: Zhang, Siqi, et al.
Publicado: (2023)

Dissipative Gradient Descent Ascent Method: A Control Theory Inspired Algorithm for Min-max Optimization
por: Zheng, Tianqi, et al.
Publicado: (2024)

A Unified Generative-Predictive Framework for Deterministic Inverse Design
por: Batley, Reza T., et al.
Publicado: (2025)

First-ish Order Methods: Hessian-aware Scalings of Gradient Descent
por: Smee, Oscar, et al.
Publicado: (2025)

Adaptive Conditional Gradient Descent
por: Khademi, Abbas, et al.
Publicado: (2025)

$k$-SVD with Gradient Descent
por: Jedra, Yassir, et al.
Publicado: (2025)

Parameter-free Mirror Descent
por: Jacobsen, Andrew, et al.
Publicado: (2022)

Faster Acceleration for Steepest Descent
por: Bai, Cedar Site, et al.
Publicado: (2024)

Mirror Descent on Riemannian Manifolds
por: Jiang, Jiaxin, et al.
Publicado: (2026)

Scaling Laws for Gradient Descent and Sign Descent for Linear Bigram Models under Zipf's Law
por: Kunstner, Frederik, et al.
Publicado: (2025)

Quadratic Gradient: A Unified Framework Bridging Gradient Descent and Newton-Type Methods by Synthesizing Hessians and Gradients
por: Chiang, John
Publicado: (2022)

Anytime Acceleration of Gradient Descent
por: Zhang, Zihan, et al.
Publicado: (2024)

Stochastic Gradient Descent with Strategic Querying
por: Jiang, Nanfei, et al.
Publicado: (2025)

Stochastic Gradient Descent with Adaptive Data
por: Che, Ethan, et al.
Publicado: (2024)

Unraveling the Gradient Descent Dynamics of Transformers
por: Song, Bingqing, et al.
Publicado: (2024)

Learning Provably Improves the Convergence of Gradient Descent
por: Song, Qingyu, et al.
Publicado: (2025)

Enhancing Fractional Gradient Descent with Learned Optimizers
por: Sobotka, Jan, et al.
Publicado: (2025)

On the Convergence of Policy in Unregularized Policy Mirror Descent
por: Lin, Dachao, et al.
Publicado: (2022)

Incremental Gauss-Newton Descent for Machine Learning
por: Korbit, Mikalai, et al.
Publicado: (2024)

Convergence of Spectral Descent for Non-smooth Optimization
por: Yang, Yixuan, et al.
Publicado: (2026)

Stochastic Zeroth order Descent with Structured Directions
por: Rando, Marco, et al.
Publicado: (2022)

Mirror Descent on Reproducing Kernel Banach Spaces
por: Kumar, Akash, et al.
Publicado: (2024)

Mirror and Preconditioned Gradient Descent in Wasserstein Space
por: Bonet, Clément, et al.
Publicado: (2024)

Derivatives of Stochastic Gradient Descent in parametric optimization
por: Iutzeler, Franck, et al.
Publicado: (2024)

Convergence of Alternating Gradient Descent for Matrix Factorization
por: Ward, Rachel, et al.
Publicado: (2023)