arXiv:2608. 03045v1 Announce Type: new Abstract: We consider grey-box optimization problems where the decision variables naturally partition into black-box variables (as arguments to an expensive black-box function) and white-box variables, governed by a set of explicit, closed-form equations that also depend on the output of the black-box function.
By Joshua E. Hammond, Tyler A. Soderstrom, Brian A. Korgel, Michael Baldea
arXiv:2607. 00865v1 Announce Type: new Abstract: Bayesian Optimisation (BO) under unknown constraints is particularly challenging when feasible regions are small.
By Hauke Maathuis, Roeland De Breuker, Saullo Castro, Maike Osborne
arXiv:2603. 24567v2 Announce Type: replace-cross Abstract: Constrained optimization in high-dimensional black-box settings is difficult due to expensive evaluations, the lack of gradient information, and complex feasibility regions.
By Raju Chowdhury, Tanmay Sen, Biswabrata Pradhan
arXiv:2505. 01258v2 Announce Type: replace-cross Abstract: Bilevel optimization has recently attracted significant attention in machine learning due to its wide range of applications and advanced hierarchical optimization capabilities.
By Tianshu Chu, Dachuan Xu, Wei Yao, Chengming Yu, Jin Zhang
arXiv:2607. 23448v1 Announce Type: cross Abstract: Expensive constrained optimization problems in real-world industry design often involve constraint thresholds that are difficult to determine in advance.
By Jin Wang, Xi Lin, Handing Wang
The paper addresses instability in bilevel optimization solutions when problem data changes. It proposes a lifted formulation for the optimistic setting that remains stable under mild assumptions, without requiring convexity or smoothness. The approach accommodates integer restrictions and disjunctive constraints, relies on pointwise and local calmness of the lower-level problem, and offers computational advantages including an outer approximation algorithm.
By Johannes O. Royset