Year: 2024
Type: preprint
Abstract: Mathematical optimization is ubiquitous in modern applications. However, in practice, we often need to use nonlinear optimization models, for which the existing optimization tools such as Cplex or Gurobi may not be directly applicable and an (error-prone) manual transformation often has to be done. Thus, to address this issue, in this paper we investigate the problem of automatically verifying and synthesizing reductions, the solution of which may allow an automatic linearization of nonlinear models. We show that the synthesis of reductions can be formulated as an $\exists^* \forall^*$ synthesis problem, which can be solved by an SMT solver via the counter-example guided inductive synthesis approach (CEGIS).
Source: arXiv (Cornell University)
Authors: Cao, Jian, Lin, Liyong, Li, Lele
Institution: Contemporary Amperex Technology Co., Limited.
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4402701333