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arXiv 2005.02667math.OC
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二次约束二次规划的有效不等式及全局解算法

Valid inequalities and global solution algorithm for Quadratically Constrained Quadratic Programs

Amélie Lambert

摘要:

我们考虑问题$(QP)$的精确解,该问题包含在极小化受二次约束的二次函数中。从经典的凸松弛的McCormick包络出发,我们引入了12个不等式,这些不等式是由$(QP)$的变量范围导出的。证明了这些一般的三角不等式割取McCormick包络的可行解。然后,我们展示了如何计算凸松弛$(P^*)$的最优值等于“肖氏加RLT加三角形”半定松弛的$(QP)$,其中包括新的不等式。我们还提出了一个启发式解决这个巨大的半定程序,作为一个分离算法。然后,我们解决了$(QP)$的全局最优性与分支定界的基础上$(P^*)$。此外,由于新的不等式涉及$(QP)$的原始变量的上下上界,它们在分支定界框架中的使用加速了整个过程。我们在unitbox实例上显示,我们的方法优于比较的求解器。

英文摘要:

We consider the exact solution of problem $(QP)$ that consists in minimizing a quadratic function subject to quadratic constraints. Starting from the classical convex relaxation that uses the McCormick's envelopes, we introduce 12 inequalities that are derived from the ranges of the variables of $(QP)$. We prove that these general Triangle inequalities cut feasible solutions of the McCormick's envelopes. We then show how we can compute a convex relaxation $(P^*)$ which optimal value equals to the "Shor's plus RLT plus Triangle" semi-definite relaxation of $(QP)$ that includes the new inequalities. We also propose a heuristic for solving this huge semi-definite program that serves as a separation algorithm. We then solve $(QP)$ to global optimality with a branch-and-bound based on $(P^*)$. Moreover, as the new inequalities involved the lower and upper bounds on the original variables of $(QP)$, their use in a branch-and-bound framework accelerates the whole process. We show on the unitbox instances that our method outperforms the compared solvers.

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