Rennala MVR: Improved Time Complexity for Parallel Stochastic Optimization via Momentum-Based Variance Reduction
- URL: http://arxiv.org/abs/2605.08871v1
- Date: Sat, 09 May 2026 10:46:59 GMT
- Title: Rennala MVR: Improved Time Complexity for Parallel Stochastic Optimization via Momentum-Based Variance Reduction
- Abstract summary: We show that under a mean-d smoothness assumption, variance reduction can improve time complexity in heterogeneous systems.<n>We propose Rennala MVR, a variance-reduced extension of Rennala SGD based on momentum-based variance reduction.
- Score: 45.88028371034407
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large-scale machine learning models are trained on clusters of machines that exhibit heterogeneous performance due to hardware variability, network delays, and system-level instabilities. In such environments, time complexity rather than iteration complexity becomes the relevant performance metric for optimization algorithms. Recent work by Tyurin and Richtárik (2023) established the first time complexity analysis for parallel first-order stochastic optimization, proposing Rennala SGD as a time-optimal method for smooth nonconvex optimization. However, Rennala SGD is fundamentally a modification of SGD, and variance reduction techniques are known to improve the iteration complexity of SGD. In this work, we investigate whether variance reduction can also improve time complexity in heterogeneous systems. We show that, under a mean-squared smoothness assumption, variance reduction can improve time complexity in relevant parameter regimes. To this end, we propose Rennala MVR, a variance-reduced extension of Rennala SGD based on momentum-based variance reduction, and analyze its oracle and time complexity. We establish lower bounds for time complexity under these assumptions. On a stochastic quadratic benchmark, experiments with the exact method support the theory, while neural-network experiments with a practical inexact variant show similar empirical gains over Rennala SGD.
Related papers
- Trajectory-Optimized Time Reparameterization for Learning-Compatible Reduced-Order Modeling of Stiff Dynamical Systems [0.2877671212386687]
Time re parameterization (TR) is a stiffness-mitigation mechanism for neural ODE reduced-order modeling.<n>This work investigates TR as a stiffness-mitigation mechanism for neural ODE reduced-order modeling and introduces a trajectory-optimized TR (TOTR) formulation.<n>ToTR is evaluated on three stiff problems: a parameterized stiff linear system, the van der Pol oscillator, and the HIRES chemical kinetics model.
arXiv Detail & Related papers (2026-03-17T14:33:59Z) - Momentum LMS Theory beyond Stationarity: Stability, Tracking, and Regret [6.655960359749289]
In large-scale data processing scenarios, data often arrive in sequential streams generated by complex systems.<n>This nonstationarity challenges theoretical analysis, as it violates classical assumptions.<n>This paper investigates the Momentum Least Mean Squares (MLMS) algorithm as an adaptive identification tool.
arXiv Detail & Related papers (2026-02-12T14:24:42Z) - Ringleader ASGD: The First Asynchronous SGD with Optimal Time Complexity under Data Heterogeneity [51.56484100374058]
We introduce Ringleader ASGD, the first asynchronous algorithm that attains the theoretical lower bounds for parallel computation.<n>Our analysis further establishes that Ringleader ASGD remains optimal under arbitrary gradient and even time-varying speeds.
arXiv Detail & Related papers (2025-09-26T19:19:15Z) - Ringmaster ASGD: The First Asynchronous SGD with Optimal Time Complexity [92.1840862558718]
Ringmaster ASGD achieves optimal time complexity under arbitrarily heterogeneous computation times.<n>This makes it the first Asynchronous SGD method to meet the theoretical lower bounds for time complexity in such scenarios.
arXiv Detail & Related papers (2025-01-27T16:07:26Z) - MindFlayer SGD: Efficient Parallel SGD in the Presence of Heterogeneous and Random Worker Compute Times [49.1574468325115]
We investigate the problem of minimizing the expectation of smooth non functions in a setting with multiple parallel workers that are able to compute optimal gradients.<n>A challenge in this context is the presence of arbitrarily heterogeneous and distributed compute times.<n>We introduce MindFlayer SGD, a novel parallel SGD method specifically designed to handle this gap.
arXiv Detail & Related papers (2024-10-05T21:11:32Z) - Freya PAGE: First Optimal Time Complexity for Large-Scale Nonconvex Finite-Sum Optimization with Heterogeneous Asynchronous Computations [92.1840862558718]
In practical distributed systems, workers typically not homogeneous, and can have highly varying processing times.
We introduce a new parallel method Freya to handle arbitrarily slow computations.
We show that Freya offers significantly improved complexity guarantees compared to all previous methods.
arXiv Detail & Related papers (2024-05-24T13:33:30Z) - Scalable Spatiotemporally Varying Coefficient Modelling with Bayesian Kernelized Tensor Regression [17.158289775348063]
Kernelized tensor Regression (BKTR) can be considered a new and scalable approach to modeling processes with low-rank cotemporal structure.
We conduct extensive experiments on both synthetic and real-world data sets, and our results confirm the superior performance and efficiency of BKTR for model estimation and inference.
arXiv Detail & Related papers (2021-08-31T19:22:23Z) - Convergence of Meta-Learning with Task-Specific Adaptation over Partial
Parameters [152.03852111442114]
Although model-agnostic metalearning (MAML) is a very successful algorithm meta-learning practice, it can have high computational complexity.
Our paper shows that such complexity can significantly affect the overall convergence performance of ANIL.
arXiv Detail & Related papers (2020-06-16T19:57:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.