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<p>The recent results of An, Luan, and Yen [Differential stability in convex optimization via generalized polyhedrality. Vietnam Journal of Mathematics <b>53</b>, 721–734 (2025)] on differential stability of parametric optimization problems described by proper generalized polyhedral convex functions and generalized polyhedral convex set-valued maps are analyzed, developed, and sharpened in this paper. Namely, keeping the Hausdorff locally convex topological vector spaces setting, we clarify the relationships between the upper estimates and lower estimates for the subdifferential and the singular subdifferential of the optimal value function. As shown by an example, the lower estimates can be strict. But, surprisingly, each upper estimate is an equality. Thus, exact formulas for the subdifferential and the singular subdifferential under consideration are obtained. In addition, it is proved that each subdifferential upper estimate coincides with the corresponding lower estimate if either the objective function or the constraint set-valued map is polyhedral&#xa0;convex.</p>

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On Differential Stability of a Class of Convex Optimization Problems

  • 期刊论文

  • 发表日期：

    2025年10月27日

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  • Nguyen Dong Yen,
  • Duong Thi Viet An,
  • Vu Thi Huong,
  • Nguyen Ngoc Luan

摘要

The recent results of An, Luan, and Yen [Differential stability in convex optimization via generalized polyhedrality. Vietnam Journal of Mathematics 53, 721–734 (2025)] on differential stability of parametric optimization problems described by proper generalized polyhedral convex functions and generalized polyhedral convex set-valued maps are analyzed, developed, and sharpened in this paper. Namely, keeping the Hausdorff locally convex topological vector spaces setting, we clarify the relationships between the upper estimates and lower estimates for the subdifferential and the singular subdifferential of the optimal value function. As shown by an example, the lower estimates can be strict. But, surprisingly, each upper estimate is an equality. Thus, exact formulas for the subdifferential and the singular subdifferential under consideration are obtained. In addition, it is proved that each subdifferential upper estimate coincides with the corresponding lower estimate if either the objective function or the constraint set-valued map is polyhedral convex.