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Technical Note—Improved Sample-Complexity Bounds in Stochastic Optimization

Author

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  • Alok Baveja

    (Department of Supply Chain Management, Rutgers Business School, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854)

  • Amit Chavan

    (Product and Engineering, Snowflake Inc., San Mateo, California 94402)

  • Andrei Nikiforov

    (School of Business, Rutgers, The State University of New Jersey, Camden, New Jersey 08102)

  • Aravind Srinivasan

    (Department of Computer Science, University of Maryland, College Park, Maryland 20742)

  • Pan Xu

    (Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey 07102)

Abstract

Real-world network-optimization problems often involve uncertain parameters during the optimization phase. Stochastic optimization is a key approach introduced in the 1950s to address such uncertainty. This paper presents improved upper bounds on the number of samples required for the sample-average approximation method in stochastic optimization. It enhances the sample complexity of existing approaches in this setting, providing faster approximation algorithms for any method that employs this framework. This work is particularly relevant for solving problems like the stochastic Steiner tree problem.

Suggested Citation

  • Alok Baveja & Amit Chavan & Andrei Nikiforov & Aravind Srinivasan & Pan Xu, 2025. "Technical Note—Improved Sample-Complexity Bounds in Stochastic Optimization," Operations Research, INFORMS, vol. 73(2), pages 986-994, March.
    • Handle: RePEc:inm:oropre:v:73:y:2025:i:2:p:986-994
    DOI: 10.1287/opre.2018.0340
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    Optimization;

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