Abraham P. Punnen

Approximate local search in combinatorial optimization

Local search algorithms for combinatorial optimization problems are in general of pseudopolynomial running time and polynomial-time algorithms are often not known for finding locally optimal solutions for NP-hard optimization problems. We introduce the concept of ε-local optimality and show that an ε-local optimum can be identified in time polynomial in the problem size and 1=ε whenever the corresponding neighborhood can be searched in polynomial time, for ε > 0. If the neighborhood can be searched in polynomial time for a Δ-local optimum, a variation of our main algorithm produces a (Δ + ε)-local optimum in time polynomial in the problem size and 1/ε. As a consequence, a combinatorial optimization problem has a fully polynomial-time approximation scheme if and only if the problem of determining a better solution---the so-called augmentation problem---has a fully polynomial-time approximation scheme.

The Traveling Salesman Problem: Applications, Formulations and Variations

The traveling salesman problem (TSP) is to find a routing of a salesman who starts from a home location, visits a prescribed set of cities and returns to the original location in such a way that the total distance travelled is minimum and each city is visited exactly once. Although a business tour of a modern day traveling salesman may not seem to be too complex in terms of route planning, the TSP in its generality represents a typical ‘hard’ combinatorial optimization problem.

Learning multicriteria fuzzy classification method PROAFTN from data

In this paper, we present a new methodology for learning parameters of multiple criteria classification method PROAFTN from data. There are numerous representations and techniques available for data mining, for example decision trees, rule bases, artificial neural networks, density estimation, regression and clustering. The PROAFTN method constitutes another approach for data mining. It belongs to the class of supervised learning algorithms and assigns membership degree of the alternatives to the classes. The PROAFTN method requires the elicitation of its parameters for the purpose of classification. Therefore, we need an automatic method that helps us to establish these parameters from the given data with minimum classification errors. Here, we propose variable neighborhood search metaheuristic for getting these parameters. The performances of the newly proposed method were evaluated using 10 cross validation technique. The results are compared with those obtained by other classification methods previously reported on the same data. It appears that the solutions of substantially better quality are obtained with proposed method than with these former ones.

TSP Heuristics: Domination Analysis and Complexity

We show that the 2-Opt and 3-Opt heuristics for the traveling salesman problem (TSP) on the complete graph Kn produce a solution no worse than the average cost of a tour in Kn in a polynomial number of iterations. As a consequence, we get that the domination numbers of the 2- Opt , 3- Opt , Carlier—Villon, Shortest Path Ejection Chain, and Lin—Kernighan heuristics are all at least (n-2)! / 2 . The domination number of the Christofides heuristic is shown to be no more than

A linear time algorithm for the maximum capacity path problem

\lceil{n}/{2}\rceil !

The quadratic minimum spanning tree problem: A lower bounding procedure and an efficient search algorithm

, and for the Double Tree heuristic and a variation of the Christofides heuristic the domination numbers are shown to be one (even if the edge costs satisfy the triangle inequality). Further, unless P = NP, no polynomial time approximation algorithm exists for the TSP on the complete digraph

Domination analysis of some heuristics for the traveling salesman problem

\vec{K}_n

Local search intensified: Very large-scale variable neighborhood search for the multi-resource generalized assignment problem

with domination number at least (n-1)!-k for any constant k or with domination number at least (n-1)! - (( k /(k+1))(n+r))!-1 for any non-negative constants r and k such that (n+r)

On: Travelling salesman problem under categorization

\equiv

A fast algorithm for a class of bottleneck problems

0 mod (k+1). The complexities of finding the median value of costs of all the tours in