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A minimum spanning tree lower-bounds metric-TSP optimum
Statement
For a metric-TSP instance, let be a minimum spanning tree of its complete weighted graph. Then .
Facts & Assumptions
Given: A metric-TSP instance with vertex set , , complete graph on , nonnegative symmetric rational lengths , and the optimal tour cost ; and a minimum spanning tree of of weight .
A feasible tour is a cyclic ordering visiting every vertex exactly once, its cost sums the consecutive lengths including the closing edge, and is the minimum of these costs, attained over the finitely many cyclic orderings; all lengths are nonnegative and every two distinct vertices are joined by an edge. (Metric traveling-salesperson problem)
A minimum spanning tree of a connected weighted graph is a spanning tree with for every spanning tree of , where . (Real edge-weighted graphs, total tree weight and minimum spanning trees)
A finite graph is connected if and only if it has a spanning tree, and a spanning tree of is a spanning subgraph with , that is connected and acyclic. (A finite graph is connected if and only if it has a spanning tree, Spanning trees of a graph)
Proof
Take an optimal tour and write its cyclic ordering as with the closing edge , so . Delete the closing edge and let be the subgraph of with vertex set and edge set . The subgraph spans and is connected: for the walk lies in and joins to . Its total length is , because lengths are nonnegative.
Since is a finite connected graph, [F3] provides a spanning tree of with and . All lengths are nonnegative, so deleting edges cannot increase total length and . In particular is also a spanning tree of the complete graph , since and .
The tree is a minimum spanning tree of the complete graph , and is a spanning tree of , so by [F2] .
Therefore every metric-TSP instance satisfies for a minimum spanning tree of its complete weighted graph. The argument uses one optimal tour only as a comparison object; it computes no optimal tour and gives a lower bound on , not an upper bound.
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Sources
- Williamson and Shmoys, The Design of Approximation Algorithms, §2.4 Lemma 2.10 and its proof, printed pp. 44–45 (standard reference, not scraped)