A graph vertex-subset problem defines which subsets of the vertices of an input graph are feasible solutions. We view a feasible solution as a set of tokens placed on the vertices of the graph. A reconfiguration variant of a vertex-subset problem asks, given two feasible solutions of size $k$, whether it is possible to transform one into the other by a sequence of token slides (along edges of the graph) or token jumps (between arbitrary vertices of the graph) such that each intermediate set remains a feasible solution of size $k$. Many algorithmic questions present themselves in the form of reconfiguration problems: Given the description of an initial system state and the description of a target state, is it possible to transform the system from its initial state into the target one while preserving certain properties of the system in the process? Such questions have received a substantial amount of attention under the so-called combinatorial reconfiguration framework. We consider reconfiguration variants of three fundamental underlying graph vertex-subset problems, namely Independent Set, Dominating Set, and Connected Dominating Set. We survey both older and more recent work on the parameterized complexity of all three problems when parameterized by the number of tokens $k$. The emphasis will be on positive results and the most common techniques for the design of fixed-parameter tractable algorithms.
翻译:图形顶点子集问题定义了输入图的顶点的哪一子子是可行的解决办法。 我们认为一个可行的解决办法是放在图形顶点上的一组符号。 一个顶点子集问题的重新配置变体要求, 给两个大小为$k$的可行解决办法, 是否可以通过一个符号幻灯片序列( 图的长边缘) 或象征性跳跃( 图形的任意垂直) 将一个子集变成另一个子集, 使每个中间部分的顶点仍然是一个大小为$k$的可行解决办法。 许多算法问题以重组问题的形式出现: 鉴于对初始系统状态的描述和对目标状态的描述, 我们能否将这个系统从最初状态转换为目标状态, 同时保留系统在这个过程中的某些特性? 这些问题在所谓的组合重新配置框架下得到了大量关注。 我们考虑的是三种基本图形顶点子子子集的重组变体变量, 即独立设置、 定点设置和连接的Domicalizing Set : 我们用最老的和最固定的参数的参数强调最近三个参数的复杂度和最精确的参数, 我们用最老和最精确的参数来测量的参数强调。