A Priori Optimization

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Abstract
Consider a complete graph G = (V, E) in which each node is present with probability p. We are interested in solving combinatorial optimization problems on subsets of nodes which are present with a certain probability. We introduce the idea of a priori optimization as a strategy competitive to the strategy of reoptimization, under which the combinatorial optimization problem is solved optimally for every instance. We consider four problems: the traveling salesman problem (TSP), the minimum spanning tree, vehicle routing, and traveling salesman facility location. We discuss the applicability of a priori optimization strategies in several areas and show that if the nodes are randomly distributed in the plane the a priori and reoptimization strategies are very close in terms of performance. We characterize the complexity of a priori optimization and address the question of approximating the optimal a priori solutions with polynomial time heuristics with provable worst-case guarantees. Finally, we use the TSP as an example to find practical solutions based on ideas of local optimality.

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