A Hybrid SMT-NRA Solver: Integrating 2D Cell-Jump-Based Local Search, MCSAT and OpenCAD

TL;DR

This paper introduces a hybrid SMT-NRA solver combining 2D cell-jump local search, MCSAT, and OpenCAD, enhancing search efficiency and performance in nonlinear real arithmetic problems.

Contribution

It presents a novel hybrid framework integrating 2D cell-jump local search, MCSAT, and OpenCAD, with new techniques like sample-cell projection to improve SMT-NRA solving.

Findings

Improved local search performance demonstrated.

Effective integration of MCSAT and OpenCAD.

Guided search reduces conflicts.

Abstract

In this paper, we propose a hybrid framework for Satisfiability Modulo the Theory of Nonlinear Real Arithmetic (SMT-NRA for short). First, we introduce a two-dimensional cell-jump move, called \emph{$2d$-cell-jump}, generalizing the key operation, cell-jump, of the local search method for SMT-NRA. Then, we propose an extended local search framework, named \emph{$2d$-LS} (following the local search framework, LS, for SMT-NRA), integrating the model constructing satisfiability calculus (MCSAT) framework to improve search efficiency. To further improve the efficiency of MCSAT, we implement a recently proposed technique called \emph{sample-cell projection operator} for MCSAT, which is well suited for CDCL-style search in the real domain and helps guide the search away from conflicting states. Finally, we present a hybrid framework for SMT-NRA integrating MCSAT, $2d$-LS and OpenCAD, to improve search efficiency through information exchange. The experimental results demonstrate improvements in local search performance, highlighting the effectiveness of the proposed methods.