Bounds on the radius and status of graphs

Two classical concepts of centrality in a graph are the median and the center. The connected notions of the status and the radius of a graph seem to be in no relation. In this paper, however, we show a clear connection of both concepts, as they obtain their minimum and maximum values at the same type of tree graphs. Trees with fixed maximum degree and extremum radius and status, resp., are characterized. The bounds on radius and status can be transferred to general connected graphs via spanning trees. A new method of proof allows not only to regain results of Lin et al. on graphs with extremum status, but it allows also to prove analogous results on graphs with extremum radius

Bounded space on-line variable-sized bin packing

In this paper we consider the fc-bounded space on-line bin packing problem. Some efficient approximation algorithms are described and analyzed. Selecting either the smallest or the largest available bin size to start a new bin as items arrive turns out to yield a worst-case performance bound of 2. By packing large items into appropriate bins, an efficient approximation algorithm is derived from fc-bounded space on-line bin packing algorithms and its worst-case performance bounds is 1.7 for k > 3

Steiner minimum trees for equidistant points on two sides of an angle

In this paper we deal with the Steiner minimum tree problem for a special type of point sets. These sets consist of the vertex of an angle 2a and equidistant points lying on the two sides of this angle

Constrained partitioning problems

AbstractWe consider partitioning problems subject to the constraint that the subsets in the partition are independent sets or bases of given matroids. We derive conditions for the functions F and [fnof] such that an optimal partition (S∗1, S∗2,…, S∗k) which minimizes F([fnof](S1),…, [fnof](S k)) has certain order properties. These order properties allow to determine optimal partitions by Greedy-like algorithms. In particular balancing partitioning problems can be solved in this way

Inverse median problems

AbstractThe inverse p-median problem consists in changing the weights of the customers of a p-median location problem at minimum cost such that a set of p prespecified suppliers becomes the p-median. The cost is proportional to the increase or decrease of the corresponding weight. We show that the discrete version of an inverse p-median problem can be formulated as a linear program. Therefore, it is polynomially solvable for fixed p even in the case of mixed positive and negative customer weights. In the case of trees with nonnegative vertex weights, the inverse 1-median problem is solvable in a greedy-like fashion. In the plane, the inverse 1-median problem can be solved in O(nlogn) time, provided the distances are measured in l1- or l∞-norm, but this is not any more true in R3 endowed with the Manhattan metric

An asymptotical study of combinatorial optimization problems by means of statistical mechanics

AbstractThe analogy between combinatorial optimization and statistical mechanics has proven to be a fruitful object of study. Simulated annealing, a metaheuristic for combinatorial optimization problems, is based on this analogy. In this paper we show how a statistical mechanics formalism can be utilized to analyze the asymptotic behavior of combinatorial optimization problems with sum objective function and provide an alternative proof for the following result: Under a certain combinatorial condition and some natural probabilistic assumptions on the coefficients of the problem, the ratio between the optimal solution and an arbitrary feasible solution tends to one almost surely, as the size of the problem tends to infinity, so that the problem of optimization becomes trivial in some sense. Whereas this result can also be proven by purely probabilistic techniques, the above approach allows one to understand why the assumed combinatorial condition is essential for such a type of asymptotic behavior

Mathematical programs with a two-dimensional reverse convex constraint

We consider the problem min{f(χ) : χ ∈ G, T(χ) ∉ int D}, where f is a lower semicontinuous function, G a compact, nonempty set in IRn, D a closed convex set in JR² with nonempty interior, and T a continuous mapping from IRn to IR². The constraint T(χ) ∉. int D is areverse convex constraint, so the feasible domain may be disconnected even when f, T are affine and G is a polytope. We show that this problem can be reduced to a quasiconcave minimization problem over a compact convex set in IR², and hence can be solved effectively provided f, T are convex and G is convex or discrete. In particular, we discuss areverse convex constraint of the form (c, χ) . (d, χ) ≤ 1. We also compare the approach in this paper with the parametric approach

Efficiently solvable special cases of hard combinatorial optimization problems

We survey some recent advances in the field of polynomially solvable special cases of hard combinatorial optimization problems like the travelling salesman problem, quadratic assignment problems and Steiner tree problems. Such special cases can be found by considering special cost structures, the geometry of the problem, the special topology of the underlying graph structure or by analyzing special algorithms. In particular we stress the importance of recognition algorithms. We comment on open problems in this area and outline some lines for future research in this field.