Digital Annealer (DA) is a computer architecture designed for tackling combinatorial optimization problems formulated as quadratic unconstrained binary optimization (QUBO) models. In this paper, we present the results of an extensive computational study to evaluate the performance of DA in a systematic way in comparison to multiple state-of-the-art solvers for different problem classes. We examine pure QUBO models, as well as QUBO reformulations of three constrained problems, namely quadratic assignment, quadratic cycle partition, and selective graph coloring, with the last two being new applications for DA. For the selective graph coloring problem, we also present a size reduction heuristic that significantly increases the number of eligible instances for DA. Our experimental results show that despite being in its development stage, DA can provide high-quality solutions quickly and in that regard rivals the state of the art, particularly for large instances. Moreover, as opposed to established solvers, within its limit on the number of decision variables, DA's solution times are not affected by the increase in instance size. These findings illustrate that DA has the potential to become a successful technology in tackling combinatorial optimization problems.

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用于二次无约束二进制优化的数字退火器：比较性能分析

数字退火器（DA）是一种计算机体系结构，旨在解决组合优化问题，这些组合优化问题被构造为二次无约束二进制优化（QUBO）模型。在本文中，我们将提供广泛的计算研究结果，以系统地评估DA的性能，并与针对不同问题类别的多个最新解决方案进行比较。我们研究了纯的QUBO模型，以及三个约束问题的QUBO重新公式化，即二次分配，二次循环划分和选择性图着色，最后两个是DA的新应用。对于选择性图形着色问题，我们还提出了大小减小启发法，该方法显着增加了DA合格实例的数量。我们的实验结果表明，尽管处于开发阶段，DA可以快速提供高质量的解决方案，并且在这方面可以与最新技术相媲美，特别是对于大型实例。此外，与已建立的求解器相反，在决策变量数量的限制内，DA的求解时间不受实例大小增加的影响。这些发现表明，DA有潜力成为解决组合优化问题的成功技术。