Golomb Ruler Problem Research Articles

Partial reformulation-linearization based optimization models for the Golomb ruler problem

In this paper, we provide a straightforward proof of a conjecture proposed in \cite{Duxbury2021} regarding the optimal solutions of a non-convex mathematical programming model of the Golomb ruler problem. Subsequently, we investigate the computational efficiency of four new binary mixed-integer linear programming models to compute optimal Golomb rulers. These models are derived from a well-known nonlinear integer model proposed in \cite{Kocuk2019}, utilizing the reformulation-linearization technique. \modif{Finally, we provide the correct outputs of the greedy heuristic proposed in \cite{Duxbury2021} and correct some false conclusions stated or implied therein.}

A conjecture on a continuous optimization model for the Golomb Ruler Problem

A Golomb Ruler (GR) is a set of integer marks along an imaginary ruler such that all the distances of the marks are different. Computing a GR of minimum length is associated to many applications (from astronomy to information theory). Although not yet demonstrated to be NP-hard, the problem is computationally very challenging. This brief note proposes a new continuous optimization model for the problem and, based on a given theoretical result and some computational experiments, we conjecture that an optimal solution of this model is also a solution to an associated GR of minimum length.

GPGPU for Difficult Black-box Problems

Difficult black-box problems arise in many scientific and industrial areas. In this paper, efficient use of a hardware accelerator to implement dedicated solvers for such problems is discussed and studied based on an example of Golomb Ruler problem. The actual solution of the problem is shown based on evolutionary and memetic algorithms accelerated on GPGPU. The presented results prove that GPGPU outperforms CPU in some memetic algorithms which can be used as a part of hybrid algorithm of finding near optimal solutions of Golomb Ruler problem. The presented research is a part of building heterogenous parallel algorithm for difficult black-box Golomb Ruler problem.

Local Search-based Hybrid Algorithms for Finding Golomb Rulers

The Golomb ruler problem is a very hard combinatorial optimization problem that has been tackled with many different approaches, such as constraint programming (CP), local search (LS), and evolutionary algorithms (EAs), among other techniques. This paper describes several local search-based hybrid algorithms to find optimal or near-optimal Golomb rulers. These algorithms are based on both stochastic methods and systematic techniques. More specifically, the algorithms combine ideas from greedy randomized adaptive search procedures (GRASP), scatter search (SS), tabu search (TS), clustering techniques, and constraint programming (CP). Each new algorithm is, in essence, born from the conclusions extracted after the observation of the previous one. With these algorithms we are capable of solving large rulers with a reasonable efficiency. In particular, we can now find optimal Golomb rulers for up to 16 marks. In addition, the paper also provides an empirical study of the fitness landscape of the problem with the aim of shedding some light about the question of what makes the Golomb ruler problem hard for certain classes of algorithm.

Equivalence of some LP-based lower bounds for the Golomb ruler problem

The Golomb Ruler problem consists in finding n integers such that all possible differences are distinct and such that the largest difference is minimum. We review three lower bounds based on linear programming that have been proposed in the literature for this problem, and propose a new one. We then show that these 4 lower bounds are equal. Finally we discuss some computational experience aiming at identifying the easiest lower bound to compute in practice.

Distinct distance sets in a graph

The problem of labelling the complete graph K n as near to graceful as possible is equivalent to the ‘Golomb ruler problem’ of finding as short a ruler as possible with n integer marks such that the distances between pairs of marks are all distinct. We generalize this to an association between labellings of K n with m-tuples and ‘distinct distance sets’ in m-dimensional grids. More generally, we define distinct distance sets for any graph G and investigate the parameter DD( G) which is the maximum size of a distinct distance set in G.