100 research outputs found
Approximability of the Subset Sum Reconfiguration Problem
The subset sum problem is a well-known NP-complete problem in which we wish to find a packing (subset) of items (integers) into a knapsack with capacity so that the sum of the integers in the packing is at most the capacity of the knapsack and at least a given integer threshold. In this paper, we study the problem of reconfiguring one packing into another packing by moving only one item at a time, while at all times maintaining the feasibility of packings. First we show that this decision problem is strongly NP-hard, and is PSPACE-complete if we are given a conflict graph for the set of items in which each vertex corresponds to an item and each edge represents a pair of items that are not allowed to be packed together into the knapsack. We then study an optimization version of the problem: we wish to maximize the minimum sum among all packings in the reconfiguration. We show that this maximization problem admits a polynomial-time approximation scheme (PTAS), while the problem is APX-hard if we are given a conflict graph
Reconfiguration of Dominating Sets
We explore a reconfiguration version of the dominating set problem, where a
dominating set in a graph is a set of vertices such that each vertex is
either in or has a neighbour in . In a reconfiguration problem, the goal
is to determine whether there exists a sequence of feasible solutions
connecting given feasible solutions and such that each pair of
consecutive solutions is adjacent according to a specified adjacency relation.
Two dominating sets are adjacent if one can be formed from the other by the
addition or deletion of a single vertex.
For various values of , we consider properties of , the graph
consisting of a vertex for each dominating set of size at most and edges
specified by the adjacency relation. Addressing an open question posed by Haas
and Seyffarth, we demonstrate that is not necessarily
connected, for the maximum cardinality of a minimal dominating set
in . The result holds even when graphs are constrained to be planar, of
bounded tree-width, or -partite for . Moreover, we construct an
infinite family of graphs such that has exponential
diameter, for the minimum size of a dominating set. On the positive
side, we show that is connected and of linear diameter for any
graph on vertices having at least independent edges.Comment: 12 pages, 4 figure
A reconfigurations analogue of Brooks’ theorem.
Let G be a simple undirected graph on n vertices with maximum degree Δ. Brooks’ Theorem states that G has a Δ-colouring unless G is a complete graph, or a cycle with an odd number of vertices. To recolour G is to obtain a new proper colouring by changing the colour of one vertex. We show that from a k-colouring, k > Δ, a Δ-colouring of G can be obtained by a sequence of O(n 2) recolourings using only the original k colours unless
G is a complete graph or a cycle with an odd number of vertices, or
k = Δ + 1, G is Δ-regular and, for each vertex v in G, no two neighbours of v are coloured alike.
We use this result to study the reconfiguration graph R k (G) of the k-colourings of G. The vertex set of R k (G) is the set of all possible k-colourings of G and two colourings are adjacent if they differ on exactly one vertex. It is known that
if k ≤ Δ(G), then R k (G) might not be connected and it is possible that its connected components have superpolynomial diameter,
if k ≥ Δ(G) + 2, then R k (G) is connected and has diameter O(n 2).
We complete this structural classification by settling the missing case:
if k = Δ(G) + 1, then R k (G) consists of isolated vertices and at most one further component which has diameter O(n 2).
We also describe completely the computational complexity classification of the problem of deciding whether two k-colourings of a graph G of maximum degree Δ belong to the same component of R k (G) by settling the case k = Δ(G) + 1. The problem is
O(n 2) time solvable for k = 3,
PSPACE-complete for 4 ≤ k ≤ Δ(G),
O(n) time solvable for k = Δ(G) + 1,
O(1) time solvable for k ≥ Δ(G) + 2 (the answer is always yes)
On Inapproximability of Reconfiguration Problems: PSPACE-Hardness and some Tight NP-Hardness Results
The field of combinatorial reconfiguration studies search problems with a
focus on transforming one feasible solution into another. Recently, Ohsaka
[STACS'23] put forth the Reconfiguration Inapproximability Hypothesis (RIH),
which roughly asserts that for some , given as input a -CSP
instance (for some constant ) over some constant sized alphabet, and two
satisfying assignments and , it is PSPACE-hard to find a
sequence of assignments starting from and ending at such that
every assignment in the sequence satisfies at least fraction of
the constraints and also that every assignment in the sequence is obtained by
changing its immediately preceding assignment (in the sequence) on exactly one
variable. Assuming RIH, many important reconfiguration problems have been shown
to be PSPACE-hard to approximate by Ohsaka [STACS'23; SODA'24].
In this paper, we prove RIH and establish the first (constant factor)
PSPACE-hardness of approximation results for many reconfiguration problems,
resolving an open question posed by Ito et al. [TCS'11]. Our proof uses known
constructions of Probabilistically Checkable Proofs of Proximity (in a
black-box manner) to create the gap. Independently, Hirahara and Ohsaka
[STOC'24] have also proved RIH.
We also prove that the aforementioned -CSP Reconfiguration problem is
NP-hard to approximate to within a factor of (for any
) when . We complement this with a -approximation polynomial time algorithm, which improves upon a -approximation algorithm of Ohsaka [2023] (again for any
). Finally, we show that Set Cover Reconfiguration is NP-hard to
approximate to within a factor of for any constant , which matches the simple linear-time 2-approximation algorithm by Ito et
al. [TCS'11]
Gap Preserving Reductions Between Reconfiguration Problems
Combinatorial reconfiguration is a growing research field studying problems on the transformability between a pair of solutions for a search problem. For example, in SAT Reconfiguration, for a Boolean formula ? and two satisfying truth assignments ?_? and ?_? for ?, we are asked to determine whether there is a sequence of satisfying truth assignments for ? starting from ?_? and ending with ?_?, each resulting from the previous one by flipping a single variable assignment. We consider the approximability of optimization variants of reconfiguration problems; e.g., Maxmin SAT Reconfiguration requires to maximize the minimum fraction of satisfied clauses of ? during transformation from ?_? to ?_?. Solving such optimization variants approximately, we may be able to obtain a reasonable reconfiguration sequence comprising almost-satisfying truth assignments.
In this study, we prove a series of gap-preserving reductions to give evidence that a host of reconfiguration problems are PSPACE-hard to approximate, under some plausible assumption. Our starting point is a new working hypothesis called the Reconfiguration Inapproximability Hypothesis (RIH), which asserts that a gap version of Maxmin CSP Reconfiguration is PSPACE-hard. This hypothesis may be thought of as a reconfiguration analogue of the PCP theorem. Our main result is PSPACE-hardness of approximating Maxmin 3-SAT Reconfiguration of bounded occurrence under RIH. The crux of its proof is a gap-preserving reduction from Maxmin Binary CSP Reconfiguration to itself of bounded degree. Because a simple application of the degree reduction technique using expander graphs due to Papadimitriou and Yannakakis (J. Comput. Syst. Sci., 1991) does not preserve the perfect completeness, we modify the alphabet as if each vertex could take a pair of values simultaneously. To accomplish the soundness requirement, we further apply an explicit family of near-Ramanujan graphs and the expander mixing lemma. As an application of the main result, we demonstrate that under RIH, optimization variants of popular reconfiguration problems are PSPACE-hard to approximate, including Nondeterministic Constraint Logic due to Hearn and Demaine (Theor. Comput. Sci., 2005), Independent Set Reconfiguration, Clique Reconfiguration, and Vertex Cover Reconfiguration
Gap Amplification for Reconfiguration Problems
In this paper, we demonstrate gap amplification for reconfiguration problems.
In particular, we prove an explicit factor of PSPACE-hardness of approximation
for three popular reconfiguration problems only assuming the Reconfiguration
Inapproximability Hypothesis (RIH) due to Ohsaka (STACS 2023). Our main result
is that under RIH, Maxmin Binary CSP Reconfiguration is PSPACE-hard to
approximate within a factor of . Moreover, the same result holds even
if the constraint graph is restricted to -expander for arbitrarily
small . The crux of its proof is an alteration of the gap
amplification technique due to Dinur (J. ACM, 2007), which amplifies the
vs. gap for arbitrarily small up to the vs.
gap. As an application of the main result, we demonstrate that
Minmax Set Cover Reconfiguration and Minmax Dominating Set Reconfiguratio} are
PSPACE-hard to approximate within a factor of under RIH. Our proof is
based on a gap-preserving reduction from Label Cover to Set Cover due to Lund
and Yannakakis (J. ACM, 1994). However, unlike Lund--Yannakakis' reduction, the
expander mixing lemma is essential to use. We highlight that all results hold
unconditionally as long as "PSPACE-hard" is replaced by "NP-hard," and are the
first explicit inapproximability results for reconfiguration problems without
resorting to the parallel repetition theorem. We finally complement the main
result by showing that it is NP-hard to approximate Maxmin Binary CSP
Reconfiguration within a factor better than .Comment: 41 pages, to appear in Proc. 35th Annu. ACM-SIAM Symp. Discrete
Algorithms (SODA), 202
Optimal PSPACE-hardness of Approximating Set Cover Reconfiguration
In the Minmax Set Cover Reconfiguration problem, given a set system
over a universe and its two covers
and of size , we wish to transform
into by repeatedly
adding or removing a single set of while covering the universe in
any intermediate state. Then, the objective is to minimize the maximize size of
any intermediate cover during transformation. We prove that Minmax Set Cover
Reconfiguration and Minmax Dominating Set Reconfiguration are
-hard to approximate within a factor of
, where is the size of the
universe and the number of vertices in a graph, respectively, improving upon
Ohsaka (SODA 2024) and Karthik C. S. and Manurangsi (2023). This is the first
result that exhibits a sharp threshold for the approximation factor of any
reconfiguration problem because both problems admit a -factor approximation
algorithm as per Ito, Demaine, Harvey, Papadimitriou, Sideri, Uehara, and Uno
(Theor. Comput. Sci., 2011). Our proof is based on a reconfiguration analogue
of the FGLSS reduction from Probabilistically Checkable Reconfiguration Proofs
of Hirahara and Ohsaka (2024). We also prove that for any constant , Minmax Hypergraph Vertex Cover Reconfiguration on
-uniform hypergraphs is
-hard to approximate within a factor of .Comment: 28 page
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