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<p>Stochastic multi-objective optimization (SMOO) has attracted increasing interest as a framework for machine learning problems with multiple objectives. The nonlinearity of the subproblem solution mapping introduces bias in the weighting parameters, which complicates the convergence analysis of multi-gradient algorithms. In this paper, we propose the Multi-gradient Stochastic Mirror Descent (MSMD) algorithm, which solves the SMOO subproblem via the stochastic mirror descent method and provides convergence guarantees. By selecting an appropriate Bregman distance-generating function, the algorithm admits a closed-form solution for the weighting vector and requires only a single stochastic gradient sample per iteration. We establish sublinear convergence rates for MSMD under four combinations of inner and outer step size strategies. A variant of MSMD for SMOO with preferences is also proposed and analyzed. Numerical experiments on benchmark test functions and neural network training tasks show that MSMD achieves competitive performance.</p>

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Mirror descent algorithm for stochastic multi-objective optimization and its application to machine learning

  • 期刊论文

  • 发表日期：

    2026年5月27日

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  • Linxi Yang,
  • Liping Tang,
  • Xinmin Yang

摘要

Stochastic multi-objective optimization (SMOO) has attracted increasing interest as a framework for machine learning problems with multiple objectives. The nonlinearity of the subproblem solution mapping introduces bias in the weighting parameters, which complicates the convergence analysis of multi-gradient algorithms. In this paper, we propose the Multi-gradient Stochastic Mirror Descent (MSMD) algorithm, which solves the SMOO subproblem via the stochastic mirror descent method and provides convergence guarantees. By selecting an appropriate Bregman distance-generating function, the algorithm admits a closed-form solution for the weighting vector and requires only a single stochastic gradient sample per iteration. We establish sublinear convergence rates for MSMD under four combinations of inner and outer step size strategies. A variant of MSMD for SMOO with preferences is also proposed and analyzed. Numerical experiments on benchmark test functions and neural network training tasks show that MSMD achieves competitive performance.