On finding short reconfiguration sequences between independent sets

09/12/2022
by   Akanksha Agrawal, et al.
0

Assume we are given a graph G, two independent sets S and T in G of size k ≥ 1, and a positive integer ℓ≥ 1. The goal is to decide whether there exists a sequence ⟨ I_0, I_1, ..., I_ℓ⟩ of independent sets such that for all j ∈{0,…,ℓ-1} the set I_j is an independent set of size k, I_0 = S, I_ℓ = T, and I_j+1 is obtained from I_j by a predetermined reconfiguration rule. We consider two reconfiguration rules. Intuitively, we view each independent set as a collection of tokens placed on the vertices of the graph. Then, the Token Sliding Optimization (TSO) problem asks whether there exists a sequence of at most ℓ steps that transforms S into T, where at each step we are allowed to slide one token from a vertex to an unoccupied neighboring vertex. In the Token Jumping Optimization (TJO) problem, at each step, we are allowed to jump one token from a vertex to any other unoccupied vertex of the graph. Both TSO and TJO are known to be fixed-parameter tractable when parameterized by ℓ on nowhere dense classes of graphs. In this work, we show that both problems are fixed-parameter tractable for parameter k + ℓ + d on d-degenerate graphs as well as for parameter |M| + ℓ + Δ on graphs having a modulator M whose deletion leaves a graph of maximum degree Δ. We complement these result by showing that for parameter ℓ alone both problems become W[1]-hard already on 2-degenerate graphs. Our positive result makes use of the notion of independence covering families introduced by Lokshtanov et al. Finally, we show that using such families one can obtain a simpler and unified algorithm for the standard Token Jumping Reachability problem parameterized by k on both degenerate and nowhere dense classes of graphs.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset
Success!
Error Icon An error occurred

Sign in with Google

×

Use your Google Account to sign in to DeepAI

×

Consider DeepAI Pro