2025/06/10 by Nuozhou Wang, Junyu Zhang, Wang, Nuozhou +3
Mathematics · #FOS: Mathematics #Optimization and Control (math.OC) #math.OC
paper · pdf · doi:10.48550/arxiv.2506.08506
arxiv created 2026/07/29 · arxiv updated 2026/07/30
In this paper, we introduce and study various algorithms for solving nonconvex minimization with inequality constraints, based on the construction of convex surrogate envelopes that majorize the objective and the constraints. In the case where the objective and constraint functions are gradient Hölderian continuous, the surrogate functions can be readily constructed and the solution method can be efficiently implemented. The surrogate envelopes are extended to the settings where the second-order information is available, and the convex subproblems are further represented by Dikin ellipsoids using the self-concordance of the convex surrogate constraints. Iteration complexities have been developed for both convex and nonconvex optimization models. The numerical results show promising potential of the proposed approaches.