# A Constant Approximation Algorithm for the *a priori* Traveling Salesman Problem

## Abstract

One of the interesting recent developments in the design and analysis of approximation algorithms has been in the area of algorithms for discrete stochastic optimization problems. In this domain, one is given not just one input, but rather a probability distribution over inputs, and yet the aim is to design an algorithm that has provably good worst-case performance, that is, for *any* probability distribution over inputs, the objective function value of the solution found by the algorithm must be within a specified factor of the optimal value.

The *a priori* traveling salesman problem is a prime example of such a stochastic optimization problem. One starts with the standard traveling salesman problem (in which one wishes to find the shortest tour through a given set of points *N*), and then considers the possibility that only a subset *A* of the entire set of points is active. The active set is given probabilistically; that is, there is a probability distribution over the subsets of *N*, which is given as part of the input. The aim is still to compute a tour through all points in *N*, but in order to evaluate its cost, we instead compute the expectation of the length of this tour after shortcutting it to include only those points in the active set *A* (where the expectation is computed with respect to the given probability distribution). The goal is to compute a “master tour” for which this expectation is minimized. This problem was introduced in the doctoral theses of Jaillet and Bertsimas, who gave asymptotic analyses when the distances between points in the input set are also given probabilistically.

In this paper, we restrict attention to the so-called “independent activation” model in which we assume that each point *j* is active with a given probability *p*_{j}, and that these independent random events. For this setting, we give a 8-approximation algorithm, a polynomial-time algorithm that computes a tour whose *a priori* TSP objective function value is guaranteed to be within a factor of 8 of optimal (and a randomized 4-approximation algorithm, which produces a tour of expected cost within a factor of 4 of optimal). This is the first constant approximation algorithm for this model.

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