Abstract
We present a GPU-CPU hybrid framework for Weighted Partial MaxSAT (WPMAX-SAT) that complements state-of-the-art CPU solvers with differentiable GPU optimization. The GPU stage directly optimizes instance-specific categorical decision parameters without a training dataset or a pretrained predictive model. The central challenge in extending differentiable optimization to WPMAX-SAT is the hard/soft clause asymmetry: hard clauses represent inviolable feasibility constraints while soft clauses carry weights. We introduce a hierarchical differentiable loss that prioritizes hard clause satisfaction before optimizing the weighted soft objective, ensuring that GPU gradient descent leans towards the constraint structure of WPMAX-SAT. Our framework transforms the GPU-optimized variable logits to high-confidence assignments and transfers them to CPU solvers via two complementary modes: partial guidance, which injects the most confident perturbed partial variable assignments as soft clauses into $N$ parallel solver instances, and phase initialization, which guides the CDCL solver's internal branching polarity with the full GPU assignment as a warm start to preserve the problem's originality. We evaluate on all 229 instances of the MSE 2024 (most recent due to cancellation in 2025) anytime weighted benchmark with the top 2 winners as baselines; our hybrid framework improves the average solution score by up to 4.9% over tt-open-wbo-inc-Glucose and 4.8% over SPB-MaxSAT-c-FPS at a 300-second time limit, with the potential to obtain better solutions surpassing the best known solution recorded in the competition evaluation.
Abstract (translated)
URL
https://arxiv.org/abs/2611.15091
