Approximation Algorithms for Multi-Criteria Traveling Salesman Problems

In multi-criteria optimization problems, several objective functions have to be optimized. Since the different objective functions are usually in conflict with each other, one cannot consider only one particular solution as the optimal solution. Instead, the aim is to compute a so-called Pareto curve of solutions. Since Pareto curves cannot be computed efficiently in general, we have to be content with approximations to them. We design a deterministic polynomial-time algorithm for multi-criteria g-metric STSP that computes (min{1 +g, 2g^2/(2g^2 -2g +1)} + eps)-approximate Pareto curves for all 1/2<=g<=1. In particular, we obtain a (2+eps)-approximation for multi-criteria metric STSP. We also present two randomized approximation algorithms for multi-criteria g-metric STSP that achieve approximation ratios of (2g^3 +2g^2)/(3g^2 -2g +1) + eps and (1 +g)/(1 +3g -4g^2) + eps, respectively. Moreover, we present randomized approximation algorithms for multi-criteria g-metric ATSP (ratio 1/2 + g^3/(1 -3g^2) + eps) for g < 1/sqrt(3)), STSP with weights 1 and 2 (ratio 4/3) and ATSP with weights 1 and 2 (ratio 3/2). To do this, we design randomized approximation schemes for multi-criteria cycle cover and graph factor problems.Comment: To appear in Algorithmica. A preliminary version has been presented at the 4th Workshop on Approximation and Online Algorithms (WAOA 2006

Approximability of Connected Factors

Finding a d-regular spanning subgraph (or d-factor) of a graph is easy by Tutte's reduction to the matching problem. By the same reduction, it is easy to find a minimal or maximal d-factor of a graph. However, if we require that the d-factor is connected, these problems become NP-hard - finding a minimal connected 2-factor is just the traveling salesman problem (TSP). Given a complete graph with edge weights that satisfy the triangle inequality, we consider the problem of finding a minimal connected $d$-factor. We give a 3-approximation for all $d$ and improve this to an (r+1)-approximation for even d, where r is the approximation ratio of the TSP. This yields a 2.5-approximation for even d. The same algorithm yields an (r+1)-approximation for the directed version of the problem, where r is the approximation ratio of the asymmetric TSP. We also show that none of these minimization problems can be approximated better than the corresponding TSP. Finally, for the decision problem of deciding whether a given graph contains a connected d-factor, we extend known hardness results.Comment: To appear in the proceedings of WAOA 201

Iterative Patching and the Asymmetric Traveling Salesman Problem

Although Branch and Bound (BnB) methods are among the most widely used techniques for solving hard problems, it is still a challenge to make these methods smarter. In this paper, we investigate iterative patching, a technique in which a fixed patching procedure is applied at each node of the BnB search tree for the Asymmetric Traveling Salesman Problem. Computational experiments show that iterative patching results in general in search trees that are smaller than the usual classical BnB trees, and that solution times are lower for usual random and sparse instances. Furthermore, it turns out that, on average, iterative patching with the Contract-or-Patch procedure of Glover, Gutin, Yeo and Zverovich (2001) and the Karp-Steele procedure are the fastest, and that ?iterative? Modified Karp-Steele patching generates the smallest search trees.

Minimum-weight Cycle Covers and Their Approximability

A cycle cover of a graph is a set of cycles such that every vertex is part of exactly one cycle. An L-cycle cover is a cycle cover in which the length of every cycle is in the set L. We investigate how well L-cycle covers of minimum weight can be approximated. For undirected graphs, we devise a polynomial-time approximation algorithm that achieves a constant approximation ratio for all sets L. On the other hand, we prove that the problem cannot be approximated within a factor of 2-eps for certain sets L. For directed graphs, we present a polynomial-time approximation algorithm that achieves an approximation ratio of O(n), where $n$ is the number of vertices. This is asymptotically optimal: We show that the problem cannot be approximated within a factor of o(n). To contrast the results for cycle covers of minimum weight, we show that the problem of computing L-cycle covers of maximum weight can, at least in principle, be approximated arbitrarily well.Comment: To appear in the Proceedings of the 33rd Workshop on Graph-Theoretic Concepts in Computer Science (WG 2007). Minor change

How to make a greedy heuristic for the asymmetric traveling salesman problem competitive

It is widely confirmed by many computational experiments that a greedy type heuristics for the Traveling Salesman Problem (TSP) produces rather poor solutions except for the Euclidean TSP. The selection of arcs to be included by a greedy heuristic is usually done on the base of cost values. We propose to use upper tolerances of an optimal solution to one of the relaxed Asymmetric TSP (ATSP) to guide the selection of an arc to be included in the final greedy solution. Even though it needs time to calculate tolerances, our computational experiments for the wide range of ATSP instances show that tolerance based greedy heuristics is much more accurate an faster than previously reported greedy type algorithms

Approximation algorithms for variants of the traveling salesman problem

The traveling salesman problem, hereafter abbreviated and referred to as TSP, is a very well known NP-optimization problem and is one of the most widely researched problems in computer science. Classical TSP is one of the original NP - hard problems [1]. It is also known to be NP - hard to approximate within any factor and thus there is no approximation algorithm for TSP for general graphs, unless P = NP. However, given the added constraint that edges of the graph observe triangle inequality, it has been shown that it is possible achieve a good approximation to the optimal solution [2]. TSP has a number of variants that have been deeply researched over the years. Approximations of varying degrees have been achieved depending on the complexity presented by the problem setup. An obvious variant is that of finding a maximum weight hamiltonian tour, also informally known as the taxicab ripoff problem . The problem is not equivalent to the minimization problem when the edge weights are non-negative and does allow good approximations. Also important is the problem when the graph is not symmetric. The problem in this case, as should be expected, is slightly tougher to approximate. Another very well researched problem is when weights of edges are drawn from the set { 1, 2}. This study was focused on gaining an understanding of these algorithms keeping in mind the primary endeavor of improving them. This thesis presents approximation algorithms for the aforementioned and other variants of the TSP, and is focused on the techniques and methods used for developing these algorithms

Deterministic algorithms for multi-criteria TSP

We present deterministic approximation algorithms for the multi-criteria traveling salesman problem (TSP). Our algorithms are faster and simpler than the existing randomized algorithms.\ud First, we devise algorithms for the symmetric and asymmetric multi-criteria Max-TSP that achieve ratios of 1/2k − ε and 1/(4k − 2) − ε, respectively, where k is the number of objective functions. For two objective functions, we obtain ratios of 3/8 − ε and 1/4 − ε for the symmetric and asymmetric TSP, respectively. Our algorithms are self-contained and do not use existing approximation schemes as black boxes.\ud Second, we adapt the generic cycle cover algorithm for Min-TSP. It achieves ratios of 3/2 + ε, $\frac{1}{2} + \frac{\gamma^3}{1-3\gamma^2} +\varepsilon$, and $\frac{1}{2} + \frac{\gamma^2}{1-\gamma} +\varepsilon$ for multi-criteria Min-ATSP with distances 1 and 2, Min-ATSP with $\gamma$-triangle inequality and Min-STSP with $\gamma$-triangle inequality, respectively

Advanced analysis of branch and bound algorithms

Als de code van een cijferslot zoek is, kan het alleen geopend worden door alle cijfercom­binaties langs te gaan. In het slechtste geval is de laatste combinatie de juiste. Echter, als de code uit tien cijfers bestaat, moeten tien miljard mogelijkheden bekeken worden. De zogenaamde 'NP-lastige' problemen in het proefschrift van Marcel Turkensteen zijn vergelijkbaar met het 'cijferslotprobleem'. Ook bij deze problemen is het aantal mogelijkheden buitensporig groot. De kunst is derhalve om de zoekruimte op een slimme manier af te tasten. Bij de Branch and Bound (BnB) methode wordt dit gedaan door de zoekruimte op te splitsen in kleinere deelgebieden. Turkensteen past de BnB methode onder andere toe bij het handelsreizigersprobleem, waarbij een kortste route door een verzameling plaatsen bepaald moet worden. Dit probleem is in algemene vorm nog steeds niet opgelost. De economische gevolgen kunnen groot zijn: zo staat nog steeds niet vast of bijvoorbeeld een routeplanner vrachtwagens optimaal laat rondrijden. De huidige BnB-methoden worden in dit proefschrift met name verbeterd door niet naar de kosten van een verbinding te kijken, maar naar de kostentoename als een verbinding niet gebruikt wordt: de boventolerantie.