Beside the DFJ formulation with cutting plane procedure there are other formulations for the TSP that we saw in class and that have a polynomial number of constraints:
the MTZ formulation (C. E. Miller, A. W. Tucker, and R. A. Zemlin, “Integer programming formulations and traveling salesman problems,” J. ACM, 7 (1960), pp. 326–329)
the Flow with Gain by Svestka (JA Svestka, “A continuous variable representation of the TSP,” Math Prog, v15, 1978, pp 211-213)
the step model by Dantzig (“Linear Programming and Extensions” 1963, Dantzig G.B.)
Your task:
Argue that the formulations are correct for the traveling salesman problem, that is, show that a solution to the traveling salesman satisfies all constraints given and that a solution with subtours would be ruled out by the formulation.
Solve the instance on the 20 random nodes with the formulations given keeping the integrality requirement for the main variables so that the optimal solution can be found. Which formulation is the fastest to solve the problem? How many branch and bound nodes are needed in each formulation to find the optimal solution?
Compare the DFJ and MTZ formulations on the basis of the quality of
their linear relaxations on the instance with random points and on the
instances dantzig42, berlin52.dat, bier127.dat. Can they all
solve these instances? Which formulation provides the tigthest LP
relaxation?
Experiment ideas to solve the instance. For example, try combining the DFJ formulation with one of those here.
Study the paper on Cut and Solve [CZ]. Implement the cut and solve method for the traveling salesman problem using the DFJ formulation as done in that paper. Compare the results with the optimal solutions from the DFJ and MTZ formulations without cut and solve.