Year: 2025
Type: conference-paper
Abstract: In fields such as autonomous and safety-critical systems, online optimization plays a crucial role in control and decision-making processes, often requiring the integration of continuous and discrete variables. These tasks are frequently modeled as mixed-integer programming (MIP) problems, where feedback data are incorporated as parameters. However, solving MIPs within strict time constraints is challenging due to their NP-complete nature. A promising solution to this challenge involves leveraging the largely invariant structure of these problems to perform most computations offline, thus enabling efficient online solving even on platforms with limited hardware capabilities. In this paper we present a novel implementation of this strategy that uses counterexample-guided inductive synthesis to split the MIP solution process into two stages. In the offline phase, we construct a mapping that provides feasible assignments for binary variables based on parameter values within a specified range. In the online phase, we solve the remaining continuous part of the problem by fixing the binary variables to the values predicted by this mapping. Our numerical evaluation demonstrates the efficiency and solution quality of this approach compared to standard mixed-integer solvers, highlighting its potential for realtime applications in resource-constrained environments.
Language: English
Subfield: Numerical Analysis
Field: Mathematics
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4410140527