{"total":32,"rows":[{"id":"2503.20762","title":"ASGO: Adaptive Structured Gradient Optimization","created":"2025-03-26","cited_by_count":57,"area":null},{"id":"2510.16981","title":"MuonBP: Faster Muon via Block-Periodic Orthogonalization","created":"2025-10-19","cited_by_count":35,"area":null},{"id":"2506.03595","title":"Purifying Shampoo: Investigating Shampoo's Heuristics by Decomposing its Preconditioner","created":"2025-06-04","cited_by_count":23,"area":null},{"id":"2509.02981","title":"AdaGrad Meets Muon: Adaptive Stepsizes for Orthogonal Updates","created":"2025-09-03","cited_by_count":23,"area":null},{"id":"2509.11983","title":"Low-rank Orthogonalization for Large-scale Matrix Optimization with Applications to Foundation Model Training","created":"2025-09-15","cited_by_count":18,"area":null},{"id":"2509.03378","title":"Understanding and Improving Shampoo and SOAP via Kullback-Leibler Minimization","created":"2025-09-03","cited_by_count":16,"area":null},{"id":"2602.08232","title":"Adaptive Matrix Online Learning through Smoothing with Guarantees for Nonsmooth Nonconvex Optimization","created":"2026-02-09","cited_by_count":14,"area":null},{"id":"2603.26554","title":"Sharp Capacity Scaling of Spectral Optimizers in Learning Associative Memory","created":"2026-03-27","cited_by_count":14,"area":null},{"id":"2506.23803","title":"SGD with Adaptive Preconditioning: Unified Analysis and Momentum Acceleration","created":"2025-06-30","cited_by_count":12,"area":null},{"id":"2603.09952","title":"On the Width Scaling of Neural Optimizers Under Matrix Operator Norms I: Row/Column Normalization and Hyperparameter Transfer","created":"2026-03-10","cited_by_count":12,"area":null},{"id":"2510.10777","title":"Preconditioned Norms: A Unified Framework for Steepest Descent, Quasi-Newton and Adaptive Methods","created":"2025-10-12","cited_by_count":11,"area":null},{"id":"2603.14315","title":"Enhancing LLM Training via Spectral Clipping","created":"2026-03-15","cited_by_count":10,"area":null},{"id":"2511.11466","title":"Non-Euclidean SGD for Structured Optimization: Unified Analysis and Improved Rates","created":"2025-11-14","cited_by_count":8,"area":null},{"id":"2602.09314","title":"Clarifying Shampoo: Adapting Spectral Descent to Stochasticity and the Parameter Trajectory","created":"2026-02-10","cited_by_count":8,"area":null},{"id":"2505.23737","title":"On the Convergence Analysis of Muon","created":"2025-05-29","cited_by_count":5,"area":null},{"id":"2509.24218","title":"Conda: Column-Normalized Adam for Training Large Language Models Faster","created":"2025-09-29","cited_by_count":5,"area":null},{"id":"2511.20584","title":"A Tale of Two Geometries: Adaptive Optimizers and Non-Euclidean Descent","created":"2025-11-25","cited_by_count":5,"area":null},{"id":"2512.00763","title":"Provable Benefit of SignGD: A Minimal Model Under Heavy-Tailed Class Imbalance","created":"2025-11-30","cited_by_count":5,"area":null},{"id":"2605.06654","title":"Optimizer-Model Consistency: Full Finetuning with the Same Optimizer as Pretraining Forgets Less","created":"2026-05-07","cited_by_count":5,"area":null},{"id":"2602.02016","title":"DASH: Faster Shampoo via Batched Block Preconditioning and Efficient Inverse-Root Solvers","created":"2026-02-02","cited_by_count":2,"area":null},{"id":"2602.03096","title":"PRISM: Structured Optimization via Anisotropic Spectral Shaping","created":"2026-02-03","cited_by_count":2,"area":null},{"id":"2604.17423","title":"A unified convergence theory for adaptive first-order methods in the nonconvex case, including AdaNorm, full and diagonal AdaGrad and Muon","created":"2026-04-19","cited_by_count":2,"area":null},{"id":"2606.02365","title":"FOAM: Frequency and Operator Error-Based Adaptive Damping Method for Reducing Staleness-Oriented Error for Shampoo","created":"2026-06-01","cited_by_count":2,"area":null},{"id":"2505.13397","title":"Learning by solving differential equations","created":"2025-05-19","cited_by_count":1,"area":null},{"id":"2601.07326","title":"Convergence Rate Analysis of the AdamW-style Shampoo: Unifying One-Sided and Two-Sided Preconditioning","created":"2026-01-12","cited_by_count":1,"area":null},{"id":"2604.02505","title":"Optimal Projection-Free Adaptive SGD for Matrix Optimization","created":"2026-04-02","cited_by_count":1,"area":null},{"id":"2604.08742","title":"Deterministic Adam-Inspired Methods with Accelerated Convergence Rate","created":"2026-04-09","cited_by_count":1,"area":null},{"id":"2605.06316","title":"Pro-KLShampoo: Projected KL-Shampoo with Whitening Recovered by Orthogonalization","created":"2026-05-07","cited_by_count":1,"area":null},{"id":"2609.21815","title":"Matrix AdaGrad: Row-wise and Column-wise Adaptive Subgradient Methods","created":"2026-09-18","cited_by_count":1,"area":null},{"id":"2606.00542","title":"Rethinking Bregman Divergences in Kronecker-Factored Optimizers","created":"2026-05-30","cited_by_count":0,"area":null},{"id":"2610.16923","title":"Minimax Optimality of Shampoo for Nonstationary Stochastic Optimization","created":"2026-10-12","cited_by_count":0,"area":null},{"id":"2610.19844","title":"Memory-Efficient Subspace Optimization with Residual Correction: From Momentum SGD to Muon","created":"2026-10-14","cited_by_count":0,"area":null}],"next_cursor":null}