In the classic TARGET SAT SELECTION problem, we are asked to minimise the number of nodes to activate so that, after the application of a certain propagation process, all nodes of the graph are active. Bazgan and Chopin [Discrete Optimization}, 14:170--182, 2014] introduced the opposite problem, named HARMLESS SET, in which they ask to maximise the number of nodes to activate such that not a single additional node is activated. In this paper we investigate how sparsity impacts the tractability of HARMLESS SET. Specifically, we answer two open questions posed by the aforementioned authors, namely a) whether the problem is FPT on planar graphs and b) whether it is FPT parametrised by treewidth. The first question can be answered in the positive using existing meta-theorems on sparse classes, and we further show that HARMLESS SET not only admits a polynomial kernel, but that it can be solved in subexponential time. We then answer the second question in the negative by showing that the problem is W[1]-hard when parametrised by a parameter that upper bounds treewidth.
翻译:在经典的 TARGET SAT Selectective 问题中,我们被要求将激活的节点数最小化, 以便在应用某种传播进程后, 图形的所有节点都是活跃的。 Bazgan 和 Chapin [Disrete Optimination}, 14: 170- 182, 2014] 提出了另一个问题, 名为 HARMLES SET, 其中他们要求最大限度地增加激活节点的数量, 这样就不会激活一个额外的节点。 在本文中, 我们调查了宽度如何影响HARMLES SET的可移动性。 具体地说, 我们回答上述作者提出的两个未决问题, 即a) 问题是否在平面图上是FPT, b) 问题是否在树枝上。 第一个问题可以用正数解答, 在稀树类上, 我们进一步表明, HARMLES SET 不仅承认一个聚点, 而且可以在次解度时间里解决它。 我们然后回答第二个问题, 当树底处显示一个问题时, 以负数 。