Year: 2024
Type: article
Abstract: As rapid response to changes becomes more imperative, optimization under uncertainty has continued to grow in both the continuous and mixed-integer fields. We design a branch-and-bound (BB) algorithm for mixed-binary nonlinear optimization problems with parameters in general locations. At every node of the BB tree we apply a state-of-the-art algorithm we have recently developed to approximately optimize parametric programs containing objectives and constraints biconvex in the variables and parameters. Numerical results are included.
Source: Journal of Global Optimization
Authors: Andrew C. Pangia, Margaret M. Wiecek
Institution: Clemson University
Language: English
Subfield: Numerical Analysis
Field: Mathematics
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,890
Awards: G2724486860, G8876996369
OpenAlex ID: W4404092308