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This paper investigates binary stochastic difference-of-convex-functions (DC) programs, which are optimization problems where the objective function is the expectation of a stochastic DC function based on a probability distribution with binary variables. Two approaches are proposed for this type of problem. The first approach involves building the sample average approximation (SAA) problem for the original problem and using DCA (DC Algorithm) and/or stochastic DCA for solving the SAA subproblem. The second approach develops a novel stochastic DCA on a continuous domain via the stochastic approximations (SA) technique.

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On Solving Discrete Stochastic Difference-of-Convex-Functions Optimization Problems

  • 会议论文

  • 发表日期：

    2025年

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  • Hoai An Le Thi,
  • Thi My Le Le

摘要

This paper investigates binary stochastic difference-of-convex-functions (DC) programs, which are optimization problems where the objective function is the expectation of a stochastic DC function based on a probability distribution with binary variables. Two approaches are proposed for this type of problem. The first approach involves building the sample average approximation (SAA) problem for the original problem and using DCA (DC Algorithm) and/or stochastic DCA for solving the SAA subproblem. The second approach develops a novel stochastic DCA on a continuous domain via the stochastic approximations (SA) technique.