Research map / Optimization / Nonconvex and smooth optimization
Continuous-time Models for Stochastic Optimization Algorithms
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Decrease the learning rate over time | SGD near a quadratic stationary point · Quadratic objective dynamics | convergence rate | Mixed |
| Increase the mini-batch size over time | MB-SGD · Non-convex optimization | convergence | Helped |
| Use variance reduction with mini-batch size greater than 1 | SVRG · Non-convex optimization | speedup | No clear effect |
Closest papers by meaning
- Convergence Rate in a Nonlinear Two-Time-Scale Stochastic Approximation with State (Time)-Dependence (AAAI 2025 · similarity 0.90)
- The Global Convergence Time of Stochastic Gradient Descent in Non-Convex Landscapes: Sharp Estimates via Large Deviations (ICML 2025 · similarity 0.89)
- Derivatives of Stochastic Gradient Descent in parametric optimization (NeurIPS 2024 · similarity 0.89)
- Stochastic optimization under time drift: iterate averaging, step-decay schedules, and high probability guarantees (NeurIPS 2021 · similarity 0.89)
- Faster Stochastic Optimization with Arbitrary Delays via Adaptive Asynchronous Mini-Batching (ICML 2025 · similarity 0.89)
- Improved Lower Bounds for First-order Stochastic Non-convex Optimization under Markov Sampling (ICML 2025 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.