In this paper, we study the independent quadratic assignment problem which is a variation of the well-known Koopmans–Beckman quadratic assignment problem. The problem is strongly NP-hard and is also hard to approximate. Some polynomially solvable special cases are identified along with a complete characterization of linearizable instances of the problem, the validity of which is shown to be verifiable in linear time. This improves the existing quadratic bound for this problem. Additional complexity results are also presented.

The independent quadratic assignment problem is a variation of the well-known Koopmans-Beckman quadratic assignment problem. The model has a wide variety of applications but it is strongly NP-hard and is also hard to approximate. In this paper we present innovative and simple heuristic algorithms to optimize the problem based on the paradigms of local search, very large-scale neighborhood (VLSN) search, and local search with path-relinking. The performance of these algorithms are analyzed using results generated by extensive computational experiments. Our experimental study highlights that the strategies of path-relinking and VLSN search when worked in unison produced superior outcomes, outperforming algorithms that employ these strategies individually. This underscores the power of hybrid algorithmic approaches in solving complex optimization problems, in particular, the independent quadratic assignment problem. This is the first experimental study available on the independent quadratic assignment problem. We also have constructed (and made available) test instances with different characteristics which can be used as benchmarks in future experimental studies on the problem.

The Steiner path problem is a common generalization of the Steiner tree and the Hamiltonian path problem, in which we have to decide if for a given graph there exists a path visiting a fixed set of terminals. In the Steiner cycle problem we look for a cycle visiting all terminals instead of a path. The Steiner path cover problem is an optimization variant of the Steiner path problem generalizing the path cover problem, in which one has to cover all terminals with a minimum number of paths. We study those problems for the special class of interval graphs. We present linear time algorithms for both the Steiner path cover problem and the Steiner cycle problem on interval graphs given as endpoint sorted lists. The main contribution is a lemma showing that backward steps to non-Steiner intervals are never necessary. Furthermore, we show how to integrate this modification to the deferred-query technique of Chang et al. to obtain the linear running times.

The bilinear assignment problem (BAP) is a generalization of the well-known quadratic assignment problem. In this paper, we study the problem from the computational analysis point of view. Several classes of neighborhood structures are introduced for the problem along with some theoretical analysis. These neighborhoods are then explored within a local search and variable neighborhood search frameworks with multistart to generate robust heuristic algorithms. In addition, we present several very fast construction heuristics. Our systematic experimental analysis disclosed some interesting properties of the BAP, different from those of comparable models. We have also introduced benchmark test instances that can be used for future experiments on exact and heuristic algorithms for the problem.

We introduce and study the combinatorial optimization problem with interaction costs (COPIC). COPIC is the problem of finding two combinatorial structures, one from each of two given families, such that the sum of their independent linear costs and the interaction costs between elements of the two selected structures is minimized. COPIC generalizes the quadratic assignment problem and many other well studied combinatorial optimization problems, and hence covers many real world applications. We show how various topics from different areas in the literature can be formulated as special cases of COPIC. The main contributions of this paper are results on the computational complexity and approximability of COPIC for different families of combinatorial structures (e.g. spanning trees, paths, matroids), and special structures of the interaction costs. More specifically, we analyze the complexity if the interaction cost matrix is parameterized by its rank and if it is a diagonal matrix. Also, we determine the structure of the intersection cost matrix, such that COPIC is equivalent to independently solving linear optimization problems for the two given families of combinatorial structures.

The quadratic minimum spanning tree problem and its variations such as the quadratic bottleneck spanning tree problem, the minimum spanning tree problem with conflict pair constraints, and the bottleneck spanning tree problem with conflict pair constraints are useful in modeling various real life applications. All these problems are known to be NP-hard. In this paper, we investigate these problems to obtain additional insights into the structure of the problems and to identify possible demarcation between easy and hard special cases. New polynomially solvable cases have been identified, as well as NP-hard instances on very simple graphs. As a byproduct, we have a recursive formula for counting the number of spanning trees on a (k,n)-accordion and a characterization of matroids in the context of a quadratic objective function.

We investigate special cases of the quadratic minimum spanning tree problem (QMSTP) on a graph $G=(V,E)$ that can be solved as a linear minimum spanning tree problem. Characterization of such problems on graphs with special properties are given. This include complete graphs, complete bipartite graphs, cactuses among others. Our characterization can be verified in $O(|E|^2)$ time. In the case of complete graphs and when the cost matrix is given in factored form, we show that our characterization can be verified in $O(|E|)$ time. Related open problems are also indicated.

We study domination analysis of algorithms for the bipartite quadratic assignment problem. A formula for the average objective function value of solutions is presented, whereas computing the median objective function value is shown to be NP-hard. An upper bound on the domination ratio of any polynomial time heuristic is given. Also, we show that heuristics that produce no worse than the average solutions have domination ratio at least 1mn. Heuristics with improved domination ratio are also presented.

In this paper we study the {\it bilinear assignment problem} (BAP) with size parameters $m$ and $n$, $m\leq n$. BAP is a generalization of the well known quadratic assignment problem and the three dimensional assignment problem and hence NP-hard. We show that BAP cannot be approximated within a constant factor unless P=NP even if the associated quadratic cost matrix $Q$ is diagonal. Further, we show that BAP remains NP-hard if $m = O(\sqrt[r]{n})$, for some fixed $r$, but is solvable in polynomial time if $m = O(\sqrt{\log n})$. When the rank of $Q$ is fixed, BAP is observed to admit FPTAS and when this rank is one, it is solvable in polynomial time under some additional restrictions. We then provide a necessary and sufficient condition for BAP to be equivalent to two linear assignment problems. A closed form expression to compute the average of the objective function values of all solutions is presented, whereas the median of the solution values cannot be identified in polynomial time, unless P=NP. We then provide polynomial time heuristic algorithms that find a solution with objective function value no worse than that of $(m-1)!(n-1)!$ solutions. However, computing a solution whose objective function value is no worse than that of $m!n!-\lceil\frac{m}{\beta}\rceil !\lceil\frac{n}{\beta}\rceil !$ solutions is NP-hard for any fixed rational number $\beta>1$.

Winner determination problem under Chamberlin-Courant system deals with the problem of selecting a fixed-size assembly from a set of candidates that minimizes the sum of misrepresentation values. This system does not restrict the candidates to have a minimum number of votes to be selected. The problem is known to be NP-hard. In this paper, we consider domination analysis of a 2-Opt heuristic for this problem. We show that the 2-Opt heuristic produces solutions no worse than the average solution in polynomial time. We also show that the domination number of the 2-Opt heuristic is at least (m-1 k-1)k(n-1) for n voters and m candidates.

An instance of a combinatorial optimization problem is said to have the constant objective value property (COVP) if every feasible solution has the same objective function value. In this paper our goal is to characterize the set of all instances with the COVP for multidimensional assignment problems.Our central result deals with planar d-dimensional assignment problems. We show that such constant objective value instances are characterized by so-called sum-decomposable arrays with appropriate parameters. This adds to the known result for the axial d-dimensional case. (C) 2016 Elsevier B.V. All rights reserved.

We first consider the weighted p-center problem, in which the centers are constrained to lie on two axis-parallel lines. Given a set of n points in the plane, which are sorted according to their x-coordinates, we show how to test in O(n log n) time if p piercing points placed on two lines, parallel to the x-axis, can pierce all the disks of different radii centered at the n given points. This leads to an O(n log(2) n) time algorithm for the weighted p-center problem. We then consider the unweighted case, where the centers are constrained to be on two perpendicular lines. Our algorithm runs in O(n log(2) n) time in this case as well.

We consider a special case of the generalized minimum spanning tree problem (GMST) and the generalized travelling salesman problem (GTSP) where we are given a set of points inside the integer grid (in Euclidean plane) where each grid cell is $1 \times 1$. In the MST version of the problem, the goal is to find a minimum tree that contains exactly one point from each non-empty grid cell (cluster). Similarly, in the TSP version of the problem, the goal is to find a minimum weight cycle containing one point from each non-empty grid cell. We give a $(1+4\sqrt{2}+\epsilon)$ and $(1.5+8\sqrt{2}+\epsilon)$-approximation algorithm for these two problems in the described setting, respectively. Our motivation is based on the problem posed in [7] for a constant approximation algorithm. The authors designed a PTAS for the more special case of the GMST where non-empty cells are connected end dense enough. However, their algorithm heavily relies on this connectivity restriction and is unpractical. Our results develop the topic further.

We study tilings of groups with mutually disjoint difference sets. Some necessary existence conditions are proved and shown not to be sufficient. In the case of tilings with two difference sets we show the equivalence to skew Hadamard difference sets, and prove that they must be normalized if the group is abelian. Furthermore, we present some constructions of tilings based on cyclotomy and investigate tilings consisting of Singer difference sets.

Given a finite nonempty set of primes S, we build a graph $\mathcal{G}$ with vertex set $\mathbb{Q}$ by connecting x and y if the prime divisors of both the numerator and denominator of x-y are from S. In this paper we resolve two conjectures posed by Ruzsa concerning the possible sizes of induced nondegenerate cycles of $\mathcal{G}$, and also a problem of Ruzsa concerning the existence of subgraphs of $\mathcal{G}$ which are not induced subgraphs.

We discuss the computational complexity of special cases of the 3-dimensional (axial) assignment problem where the elements are points in a Cartesian space and where the cost coefficients are the perimeters of the corresponding triangles measured according to a certain norm. (All our results also carry over to the corresponding special cases of the 3-dimensional matching problem.) The minimization version is NP-hard for every norm, even if the underlying Cartesian space is 2-dimensional. The maximization version is polynomially solvable, if the dimension of the Cartesian space is fixed and if the considered norm has a polyhedral unit ball. If the dimension of the Cartesian space is part of the input, the maximization version is NP-hard for every $L_p$ norm; in particular the problem is NP-hard for the Manhattan norm $L_1$ and the Maximum norm $L_{\infty}$ which both have polyhedral unit balls.

Given an n× n× p cost array C we consider the problem p-P3AP which consists in finding p pairwise disjoint permutations φ_1,φ_2,…,φ_p of {1,…,n} such that ∑_k=1^p∑_i=1^nc_iφ_k(i)k is minimized. For the case p=n the planar 3-dimensional assignment problem P3AP results. Our main result concerns the p-P3AP on cost arrays C that are layered Monge arrays. In a layered Monge array all n× n matrices that result from fixing the third index k are Monge matrices. We prove that the p-P3AP and the P3AP remain NP-hard for layered Monge arrays. Furthermore, we show that in the layered Monge case there always exists an optimal solution of the p-3PAP which can be represented as matrix with bandwidth ≤ 4p-3. This structural result allows us to provide a dynamic programming algorithm that solves the p-P3AP in polynomial time on layered Monge arrays when p is fixed.

Given a combinatorial optimization problem, we aim at characterizing the set of all instances for which every feasible solution has the same objective value. Our central result deals with multi-dimensional assignment problems. We show that for the axial and for the planar $d$-dimensional assignment problem instances with constant objective value property are characterized by sum-decomposable arrays. We provide a counterexample to show that the result does not carry over to general $d$-dimensional assignment problems. Our result for the axial $d$-dimensional assignment problem can be shown to carry over to the axial $d$-dimensional transportation problem. Moreover, we obtain characterizations when the constant objective value property holds for the minimum spanning tree problem, the shortest path problem and the minimum weight maximum cardinality matching problem.