Optimization problems are fundamental in diverse fields, such as engineering, economics, and scientific computing. However, current algorithms are mostly designed for specific problem types and exhibit limited generality in solving multiple types of optimization problems. To enhance generality, we propose an automated reduction method named OP-to-MaxSAT reduction and a general optimization solver based on OP-to-MaxSAT reduction (GORED). GORED unifies the solving of multiple types of optimization problems by reducing the problems from optimization problems to MaxSAT instances in polynomial time and solving them using the state-of-the-art MaxSAT solver. The generality and solution quality of GORED are validated through experiments on 136 instances across 11 types of optimization problems. Experimental results demonstrate that GORED not only successfully solves a wide range of optimization problems but also yields solutions comparable in quality to those from existing methods, with no statistically significant differences observed. By introducing automated reduction, this work shifts the paradigm of optimization solvers from designing specialized algorithms for each problem type to employing a single algorithm for diverse problems. As a result, advances in this single algorithm can now drive progress in a wide range of optimization problems across various domains.

翻译：优化问题在工程、经济学和科学计算等不同领域中都至关重要。然而，当前的算法大多针对特定问题类型设计，在求解多种类型的优化问题时通用性有限。为了提升通用性，我们提出了一种名为OP-to-MaxSAT归约的自动化归约方法，以及一个基于此归约的通用优化求解器（GORED）。GORED通过将优化问题在多项式时间内归约为MaxSAT实例，并利用最先进的MaxSAT求解器进行求解，从而统一处理多种类型的优化问题。通过在涵盖11种优化问题类型的136个实例上进行实验，验证了GORED的通用性和求解质量。实验结果表明，GORED不仅能成功求解广泛的优化问题，而且其解的质量与现有方法相当，未观察到统计上的显著差异。通过引入自动化归约，这项工作将优化求解器的范式从为每种问题类型设计专门的算法，转变为使用单一算法处理多种问题。因此，这一单一算法的进步将能够推动跨领域广泛优化问题的发展。