Sensitivity Analysis for Shortest Hamiltonian Path and Traveling Salesman Problems

Abstract

The problem considered in this paper belongs to so called sensitivity analysis in combinatorial optimization. This term is used for a phase of solution procedure when an optimal solution of problem has been already found and additional calculations are performed in order to investigate, how this optimal solution depends on changes of problems parameters. In this paper two well known combinatorial optimization problems are considered: the shortest Hamiltonian path problem in undirected weighted graph and the symmetric traveling salesman problem. It is assumed that an optimal solution of given problem is known. The goal of sensitivity analysis consists in finding by how much we can perturb each edge weight individually without changing the optimality of the solution. The maximum increment and decrement of the edge weight that preserve the optimality of solution are called the edge tolerances with respect to this solution. In this paper, a method of computing lower bounds of the edge tolerances with respect to the optimal solution of the shortest Hamiltonian path problem and traveling salesman problem is described. The method is based on solving the sensitivity analysis problem for appropriate relaxation of the original optimization problem. This paper gives a description of the approach and its microcomputer implementation and we report preliminary results of computational experiments. (kr)

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1988
Accession Number
ADA197167

Entities

People

  • Marek Libura

Organizations

  • Carnegie Mellon University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Analogs
  • Computations
  • Computer Programming
  • Computers
  • Equations
  • Heuristic Methods
  • Inequalities
  • Linear Programming
  • Notation
  • Optimization
  • Personal Computers
  • Sensitivity
  • Simplex Method
  • Trees (Data Structures)

Readers

  • Calculus or Mathematical Analysis
  • Computational Modeling and Simulation
  • Graph Algorithms and Convex Optimization.