Research map / Optimization / Optimizers
Regularized Nonlinear Acceleration
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| apply RMPE extrapolation (k=5, with restarts) to gradient method iterates | Gradient method · Madelon logistic regression (tau=10^2) | f(x_k) - f(x*) | Helped |
| add regularization (lambda>0) to AMPE extrapolation (ablation) | AMPE · Madelon logistic regression (tau=10^2) | f(x_k) - f(x*) | Helped |
| add inexact line search after RMPE extrapolation (ablation) | RMPE 5 · Madelon logistic regression (tau=10^2) | f(x_k) - f(x*) | No clear effect |
Closest papers by meaning
- Optimal Acceleration for Minimax and Fixed-Point Problems is Not Unique (ICML 2024 · similarity 0.88)
- A simpler approach to accelerated optimization: iterative averaging meets optimism (ICML 2020 · similarity 0.88)
- Accelerated Quasi-Newton Proximal Extragradient: Faster Rate for Smooth Convex Optimization (NeurIPS 2023 · similarity 0.87)
- Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization (ICML 2020 · similarity 0.87)
- Accelerating Optimization via Differentiable Stopping Time (NeurIPS 2025 · similarity 0.86)
- Large Stepsizes Accelerate Gradient Descent for Regularized Logistic Regression (NeurIPS 2025 · similarity 0.85)
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