Quadratic Unconstrained Binary Optimization models are useful for solving a diverse range of optimization problems. Constraints can be added by incorporating quadratic penalty terms into the objective, often with the introduction of slack variables needed for conversion of inequalities. This transformation can lead to a significant increase in the size and density of the problem. Herein, we propose an efficient approach for recasting inequality constraints that reduces the number of linear and quadratic variables. Experimental results illustrate the efficacy.
翻译:二次曲线不受限制的二进制优化模式有助于解决各种各样的优化问题,通过在目标中加入二次惩罚条款可以增加限制,通常是引入不平等转换所需的松懈变量。这种转变可以导致问题的规模和密度大幅增加。在这里,我们提出一种有效的方法来重新确定不平等制约,以减少线性变量和四进变量的数量。实验结果可以说明效果。