Stochastic Optimization

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Overview

Stochastic optimization seeks good decisions when exact objectives or gradients are unavailable, observations are noisy, or the environment changes, making it central to machine learning, planning, and resource allocation. Common approaches include stochastic gradient descent and adaptive optimizers, variance reduction, mirror and proximal methods, and online or bandit algorithms; robust and risk-sensitive methods address heavy-tailed noise and costly failures. Researchers evaluate these methods through convergence, sample-complexity, and regret guarantees, as well as experiments in deep-model training, constrained planning, and simulation. A continuing challenge is to make guarantees hold under realistic noise, dependence, and changing data while keeping computation, communication, and tuning costs manageable.

Latest papers

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210 papers34 papers in the last four weeks, up 70% on the four weeks before. 0.2% of all new papers.

  1. A Tight Lower Bound for Smooth Nonconvex Stochastic Optimization with Bounded Gradient Noise

    Aug 10, 2026Jikai JinStochastic OptimizationNonconvex Stochastic Optimization

  2. On Same-Sample and Independent-Sample Stochastic Extragradient for Monotone Variational Inequalities

    Aug 6, 2026TaeHo Yoon, Nicolas LoizouStochastic OptimizationLast-Iterate Convergence

    Department of Applied Mathematics and Statistics & Mathematical Institute for Data Science, Johns Hopkins University.

  3. A proximal subgradient method for nonconvex stochastic optimization under the Kurdyka-Łojasiewicz condition

    Aug 5, 2026Felipe Atenas, Alejandro Jofré, Pedro Pérez-Aros +1Stochastic OptimizationNonconvex Stochastic Optimization

    Centro de Modelamiento Matem´atico (CNRS IRL2807), Universidad de Chile, Santiago, Chile · Departamento de Ingenier´ıa Matem´atica and Centro de Modelamiento Matem´atico (CNRS IRL2807), Universidad de Chile, Santiago, Chile · Department of Mathematics, University of Alicante, Alicante, Spain

  4. On MUON optimization: From non-convergence to an error analysis with Polar Express and the Newton-Schulz polynomial from implementations

    Aug 5, 2026Thang Do, Steffen Dereich, Arnulf JentzenStochastic OptimizationNewton-Schulz Iteration

    School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), China · Department of Probability and Statistics, Institute of Mathematics, Vietnam Academy of Science and Technology, Vietnam · Institute for Mathematical Stochastics, Faculty of Mathematics and Computer Science, University of Münster, Germany +2

  5. Robust General Utility for Reinforcement Learning

    Aug 4, 2026Zixuan Liu, Fangzheng Wu, Brian Summa +1Stochastic OptimizationReinforcement Learning

    Department of Computer Science, Tulane University, New Orleans, LA, 70118, USA

  6. Stochastic Saddle Avoidance Beyond Unit Excitation and Smoothness: A Pathwise Lyapunov-Perron Framework

    Aug 4, 2026Junwen Qiu, Bohao Ma, Andre Milzarek +1Stochastic OptimizationNonconvex Stochastic Optimization

    Industrial Systems Engineering and Management, National University of Singapore, Singapore. · School of Data Science, The Chinese University of Hong Kong, Shenzhen, China.

  7. Particle-based Generalised Stochastic Optimisation

    Aug 3, 2026Jiechen Jackie Zhang, O. Deniz AkyildizStochastic OptimizationLangevin Dynamics

    Department of Mathematics, Imperial College London

  8. Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input

    Aug 3, 2026So Nakashima, Tetsuya J. KobayashiStochastic OptimizationEvolution Strategies

    Institute of Industrial Science The University of Tokyo · Institute of Industrial Science, The University of Tokyo · Universal Biology Institute, The University of Tokyo

  9. Heterogeneous Multi-Agent Reinforcement Learning for Radio Resource Management under Coupled Finite-Horizon Constraints

    Aug 3, 2026Yeonseo Jeong, Wonhyeok Ko, Sungweon Hong +1Stochastic OptimizationFair Resource Allocation

    Department of Electronic Engineering, Hanyang University, Seoul, Korea · Department of AI-Semiconductor, Hanyang University, Seoul, Korea

  10. Adaptivity via a Parallel Architecture for Stochastic Gradient Methods

    Jul 31, 2026Bin FuStochastic OptimizationStochastic Optimization Convergence

    University of Texas Rio Grande Valley

  11. On the robustness of noisy solutions in non-convex neural networks

    Jul 29, 2026Enrico M. Malatesta, Alessandra Passalacqua, Riccardo ZecchinaStochastic OptimizationNeural Network Generalization

    Department of Computing Sciences, Bocconi University, Milano, Italy · Bocconi Institute for Data Science and Analytics (BIDSA)

  12. Variance-Reduced Conditional Gradient Methods under Markovian Sampling for Nonconvex Composite Optimization

    Jul 28, 2026Zhaojun PengStochastic OptimizationNonconvex Stochastic Optimization

    School of Computer Science Nanjing University of Information Science and Technology

  13. Closed-Loop Generative Selection: Convergence, Memory, and Noisy Oracles

    Jul 24, 2026Konstantin Fackeldey, Christof SchütteStochastic OptimizationRuntime Analysis of Evolutionary Algorithms

    Zuse Institute Berlin, Takustraße 7, 14195 Berlin, Germany · Institute for Mathematics, Technical University Berlin, Fasanenstr 89,10623 Berlin, Germany · Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 6, 14195 Berlin, Germany

  14. Weak-to-Strong Learning in Decision Making

    Jul 20, 2026Jingwei Ji, Renyuan XuStochastic OptimizationDecision-Focused Learning

    Management Science and Engineering, Stanford University

  15. Task-Space Constrained Stochastic Trajectory Optimization for Time-Optimal Forestry Crane Motion Planning

    Jul 20, 2026Marc-Philip Ecker, Christoph Fröhlich, Bernhard Bischof +2Stochastic OptimizationConstrained Motion Planning

    Automation & Control Institute (ACIN), TU Wien, 1040 Vienna, Austria · Center for Vision, Automation & Control, AIT Austrian Institute of Technology GmbH, 1210 Vienna, Austria

  16. Is Model Instability just Noise to be Tolerated or a Property that can be Managed?

    Jul 11, 2026Amirali Rayegan, Lunxiao Li, Tim MenziesStochastic OptimizationAutomated Software Engineering

    North Carolina State University Raleigh, North Carolina, USA

  17. MAGE: Understanding Stability-Performance Trade-offs in Multi-component Prompt Optimization

    Jul 11, 2026Prateek SinghStochastic OptimizationLarge Language Model-Guided Optimization

  18. How much Data do We Need? Sequential Data Collection for Stochastic Programming

    Jul 11, 2026Xin Li, Juergen Branke, Xuan Vinh DoanStochastic OptimizationSequential Decision Making

    University of Warwick

  19. Decentralized Stochastic Subgradient-type Methods with Communication Compression for Nonsmooth Nonconvex Optimization

    Jul 2, 2026Siyuan Zhang, Nachuan Xiao, Xin LiuStochastic OptimizationNonconvex Optimization

  20. Optimizer Memory Makes Shuffle Order a First-Order Source of Fine-Tuning Noise

    Jun 28, 2026John SweeneyStochastic OptimizationFine-Tuning

  21. Analysis of Adam Algorithms for Stochastic Dynamic Systems

    Jun 27, 2026Xin Zheng, Yifei Jin, Lei GuoStochastic OptimizationStochastic Optimization Convergence

    State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China · School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China · School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, Beijing 101408, China

  22. Random Walk on Bézier Curves for Global Optimization

    Jun 25, 2026Jinpeng Wang, Xingguo Xu, Yujing Sun +3Stochastic OptimizationEvolutionary Optimization

    aSchool of Computer Science, Northwestern Polytechnical University, Xi’an, 710129, China · bSchool of Mathematical Sciences, Dalian University of Technology, Dalian, 116024, China · cSchool of Mathematics, Hefei University of Technology, Hefei, 230601, China +3

  23. High-Probability PL-SGD with Markovian Noise: Optimal Mixing and Tail Dependence

    Jun 24, 2026Dhruv Sarkar, Aprameyo Chakrabartty, Vaneet AggarwalStochastic OptimizationOptimization Convergence Analysis

    Indian Institute of Technology Kharagpur · Mohamed bin Zayed University of Artificial Intelligence · Purdue University

  24. Is Variational Monte Carlo Robust? Sharp Moment Thresholds and Heavy-tailed Stochastic Optimization

    Jun 24, 2026Philipp Grohs, Davide NobileStochastic OptimizationVariational Monte Carlo

    Faculty of Mathematics, University of Vienna · RICAM, Austrian Academy of Sciences

  25. Learning Optimization Proxies for Sequential Contextual Stochastic Programs: An Order Fulfillment Application

    Jun 24, 2026Tinghan Ye, Shuaicheng Tong, Changkun Guan +2Stochastic OptimizationLearning to Optimize

    H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332 · Tippie College of Business, University of Iowa, Iowa City, IA 52242

  26. New Bounds for the Last Iterate of the Stochastic subGradient Method

    Jun 23, 2026Guglielmo Beretta, Tommaso Cesari, Roberto Colomboni +1Stochastic OptimizationLast-Iterate Convergence

    Department of Computer Science, Aarhus University, Aarhus, Denmark · School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada · School of Mathematics, University of Bristol, Bristol, United Kingdom

  27. Open Problem: Is AdamW Effective Under Heavy-Tailed Noise?

    Jun 22, 2026Dingzhi Yu, Hongyi Tao, Yuanyu Wan +2Stochastic OptimizationHeavy-Tailed Noise

    1Nanjing University · 2Zhejiang University · 3Fudan University

  28. Clipping the Price of Adaptivity at the Tail

    Jun 21, 2026Itai Kreisler, Yair Carmon, Oliver HinderStochastic OptimizationStochastic Optimization Convergence

    Tel Aviv University · University of Pittsburgh

  29. Federated learning with heavy-tailed gradient noise and communication noise: a variance-reduction based algorithm

    Jun 21, 2026Shengchao Zhao, Yongchao LiuStochastic OptimizationNonconvex Stochastic Optimization

    School of Mathematics, China University of Mining and Technology, Xuzhou, China · School of Mathematical Sciences, Dalian University of Technology, Dalian, China

  30. Distribution-Aware Robust Bilevel Optimization: Quantile-Guided Huber Updates in Two-Timescale Stochastic Approximation

    Jun 21, 2026Zhiyu Li, Xi Xuan, Davide CarboneStochastic OptimizationStochastic Approximation

    University of Science and Technology of China Hefei, China · City University of Hong Kong Hong Kong SAR, China · Laboratoire de Physique de l’École Normale Supérieure, Université PSL, CNRS, Sorbonne Université, Université de Paris Paris, France