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This chapter discusses various algorithms or declinations thereof for solving chance-constrained optimization problems. It begins with non-smooth approaches in the presence of convexity (for the feasible sets), then moves to a global optimization approach to (mixed-integer) joint chance-constrained optimization problems with right-hand side uncertainty, and finally discusses approximation techniques of different kinds, including Difference-of-Convex and CVaR approximations.

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Methods for Handling Probability Constraints

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  • 发表日期：

    2025年

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  • Wim Stefanus van Ackooij,
  • Welington Luis de Oliveira

摘要

This chapter discusses various algorithms or declinations thereof for solving chance-constrained optimization problems. It begins with non-smooth approaches in the presence of convexity (for the feasible sets), then moves to a global optimization approach to (mixed-integer) joint chance-constrained optimization problems with right-hand side uncertainty, and finally discusses approximation techniques of different kinds, including Difference-of-Convex and CVaR approximations.