Semidefinite Programming (SDP) is a powerful technique to compute tight lower bounds for Optimal Power Flow (OPF) problems. Even using clique decomposition techniques, semidefinite relaxations are still computationally demanding. However, there are many different clique decompositions for the same SDP problem and they are not equivalent in terms of computation time. In this paper, we propose a new strategy to compute efficient clique decompositions with a clique merging heuristic. This heuristic is based on two different estimates of the computational burden of an SDP problem: the size of the problem and an estimation of a per-iteration cost for a state-of-the-art interior-point algorithm. We compare our strategy with other algorithms on MATPOWER instances and we show a significant decrease in solver time.

中文翻译：

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解决最优潮流问题的半确定性松弛的集团合并算法

半定性编程（SDP）是一种用于计算最佳潮流（OPF）问题的严格下限的强大技术。即使使用派系分解技术，半确定弛豫仍然需要计算。但是，对于同一个SDP问题，有许多不同的派系分解，它们在计算时间上并不等效。在本文中，我们提出了一种新的策略，用于使用合并合并的启发式算法来计算有效的分组分解。此启发式方法基于对SDP问题的计算负担的两种不同估计：问题的大小和最新内点算法的每次迭代成本的估计。我们在MATPOWER实例上将我们的策略与其他算法进行了比较，结果表明求解器时间显着减少。