标题： 通过二次非凸重表述增强二次规划求解器 显示英文标题

标题： Enhancing Quadratic Programming Solvers via Quadratic Nonconvex Reformulation

摘要： 在本文中，我们考虑使用现代求解器如Gurobi和SCIP来解决非凸二次规划问题。众所周知，二次凸化重写的经典技术可以提高全局求解器对混合整数二次优化问题的计算效率。相比之下，二次非凸重写（QNR）的使用此前尚未被探索。本文引入了一个QNR框架——一种非传统但非常有效的方法，用于提高最新二次规划求解器如Gurobi和SCIP的性能。我们在各种非凸二次规划问题实例上进行的计算实验表明，QNR可以显著加速Gurobi和SCIP。值得注意的是，使用QNR，Gurobi在多个基准和随机生成的实例上达到了最先进水平。 显示英文摘要

摘要： In this paper, we consider solving nonconvex quadratic programming problems using modern solvers such as Gurobi and SCIP. It is well-known that the classical techniques of quadratic convex reformulation can improve the computational efficiency of global solvers for mixed-integer quadratic optimization problems. In contrast, the use of quadratic nonconvex reformulation (QNR) has not been previously explored. This paper introduces a QNR framework--an unconventional yet highly effective approach for improving the performance of state-of-the-art quadratic programming solvers such as Gurobi and SCIP. Our computational experiments on diverse nonconvex quadratic programming problem instances demonstrate that QNR can substantially accelerate both Gurobi and SCIP. Notably, with QNR, Gurobi achieves state-of-the-art performance on several benchmark and randomly generated instances.