Complexity and Algorithms for Unary Translocation Distance

2026-06-06

AI Summary

This study addresses the unary transposition distance problem: given a source multiset and a target set of integers, determine the minimum number of unary transposition operations required to transform the source into a superset containing the target. The problem is shown for the first time to be strongly NP-hard. For the special case of fixed-size target sets, a pseudo-polynomial exact algorithm is developed, while for the general case, the work proposes several approximation and parameterized algorithms alongside an integer linear programming (ILP) formulation. Key contributions include a 2-approximation algorithm—empirically outperforming heuristic baselines—a proof that the integrality gap of the LP relaxation of the ILP model is at least 4/3, and an efficient heuristic combining beam search with simulated annealing.

Abstract

Given a finite set of integers $A$, a \emph{unary translocation} produces a new set $A' = A \cup \{u,v\}$, where $u$ and $v$ are nonnegative integers satisfying $x+y=u+v$ for some $x,y\in A$. For an input set $A$ and a target set $B$, the \emph{unary translocation distance} is the minimum number of unary translocations required to obtain a superset containing $B$. In this paper, we study this problem from both theoretical and computational perspectives. We prove that computing the unary translocation distance is strongly NP-hard, thereby answering an open question raised by \citet{ConstantinMiclausPopa2026UnaryTranslocation}. On the positive side, we give an exact pseudo-polynomial algorithm for every fixed constant value of $|B|$, extending our previous results for $|B|\leq 2$. For arbitrary target sets, we present a $2$-approximation algorithm, an additive $(|B|-1)$-approximation algorithm, and show that the additive algorithm also yields a $3$-approximation. We also propose parameterized algorithms, including algorithms parameterized by the maximum value in the input set together with the optimum distance, and by the maximum value in the target set together with $|B|$. In addition, we propose an integer linear programming formulation that gives an exact mathematical model for the problem, analyze its size, and show that the LP relaxation has integrality gap at least $\frac{4}{3}$. Finally, we report computational experiments comparing the $2$-approximation algorithm, beam search, and simulated annealing. The results show that the approximation algorithm is highly effective in practice and often outperforms the heuristic baselines.

Problem

• unary translocation distance
• NP-hard
• approximation algorithm
• parameterized algorithm
• integer linear programming

Innovation

• unary translocation distance
• strongly NP-hard
• pseudo-polynomial algorithm
• approximation algorithm
• parameterized algorithm