Bounding-focused discretization methods for the global optimization of nonconvex semi-infinite programs

Evren M. Turan (), Johannes Jäschke () and Rohit Kannan ()
Additional contact information
Evren M. Turan: Norwegian University of Science and Technology (NTNU), Department of Chemical Engineering
Johannes Jäschke: Norwegian University of Science and Technology (NTNU), Department of Chemical Engineering
Rohit Kannan: Virginia Tech, Grado Department of Industrial and Systems Engineering

Computational Optimization and Applications, 2025, vol. 92, issue 3, No 10, 1035-1068

Abstract: Abstract We use sensitivity analysis to design bounding-focused discretization (cutting-surface) methods for the global optimization of nonconvex semi-infinite programs (SIPs). We begin by formulating the optimal bounding-focused discretization of SIPs as a max-min problem and propose variants that are more computationally tractable. We then use parametric sensitivity theory to design an effective heuristic approach for solving these max-min problems. We also show how our new iterative discretization methods may be modified to ensure that the solutions of their discretizations converge to an optimal solution of the SIP. We then formulate optimal bounding-focused generalized discretization of SIPs as max-min problems and design heuristic algorithms for their solution. Numerical experiments on standard nonconvex SIP test instances from the literature demonstrate that our new bounding-focused discretization methods can significantly reduce the number of iterations for convergence relative to a state-of-the-art feasibility-focused discretization method.

Keywords: Semi-infinite programming; Robust optimization; Discretization; Global optimization; Cutting-surface; Sensitivity analysis; 90C26; 90C31; 90C34; 90C47; 65K05 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10589-025-00710-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:coopap:v:92:y:2025:i:3:d:10.1007_s10589-025-00710-y

Ordering information: This journal article can be ordered from
http://www.springer.com/math/journal/10589

DOI: 10.1007/s10589-025-00710-y

Access Statistics for this article

Computational Optimization and Applications is currently edited by William W. Hager

More articles in Computational Optimization and Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().