We study integrality gaps and approximability of two closely related problems on directed graphs. Given a set V of n nodes in an underlying asymmetric metric and two specified nodes s and t, both problems ask to find an s-t path visiting all other nodes. In the asymmetric traveling salesman path problem (ATSPP), the objective is to minimize the total cost of this path. In the directed latency problem, the objective is to minimize the sum of distances on this path from s to each node. Both of these problems are NP-hard. The best known approximation algorithms for ATSPP had ratio O(log n) until the very recent result that improves it to O(log n log log n). However, only a bound of O(sqrt(n)) for the integrality gap of its linear programming relaxation has been known. For directed latency, the best previously known approximation algorithm has a guarantee of O(n^(1 2+eps)), for any constant eps > 0. We present a new algorithm for the ATSPP problem that has an approximation ratio of O(log n), but whose analysis also bounds the integrality gap of the standard LP relaxation of ATSPP by the same factor. This solves an open problem posed by Chekuri and Pal [2007]. We then pursue a deeper study of this linear program and its variations, which leads to an algorithm for the k-person ATSPP (where k s-t paths of minimum total length are sought) and an O(log n)-approximation for the directed latency problem.
Both ATSPP and the directed latency problem are closely related to the classical Traveling Salesman Problem (TSP), which asks to find the cheapest Hamiltonian cycle in a complete undirected graph with edge costs @cite_11 @cite_4 . In general weighted graphs, TSP is not approximable. However, in most practical settings it can be assumed that edge costs satisfy the triangle inequality (i.e. @math ). Though metric TSP is still NP-hard, the well-known algorithm of Christofides @cite_0 has an approximation ratio of 3 2 . Later the analysis in @cite_8 @cite_1 showed that this approximation algorithm actually bounds the integrality gap of a linear programming relaxation for TSP known as the Held-Karp LP. This integrality gap is also known to be at least @math . Furthermore, for all @math , approximating TSP within a factor of @math is NP-hard @cite_28 . Christofides' heuristic was adapted to the problem of finding the cheapest Hamiltonian path in a metric graph with an approximation guarantee of 3 2 if at most one endpoint is specified or 5 3 if both endpoints are given @cite_3 .
@cite_4 @cite_8 @cite_28 @cite_1 @cite_3 @cite_0 @cite_11
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We study integrality gaps and approximability of two closely related problems on directed graphs. Given a set V of n nodes in an underlying asymmetric metric and two specified nodes s and t, both problems ask to find an s-t path visiting all other nodes. In the asymmetric traveling salesman path problem (ATSPP), the objective is to minimize the total cost of this path. In the directed latency problem, the objective is to minimize the sum of distances on this path from s to each node. Both of these problems are NP-hard. The best known approximation algorithms for ATSPP had ratio O(log n) until the very recent result that improves it to O(log n log log n). However, only a bound of O(sqrt(n)) for the integrality gap of its linear programming relaxation has been known. For directed latency, the best previously known approximation algorithm has a guarantee of O(n^(1 2+eps)), for any constant eps > 0. We present a new algorithm for the ATSPP problem that has an approximation ratio of O(log n), but whose analysis also bounds the integrality gap of the standard LP relaxation of ATSPP by the same factor. This solves an open problem posed by Chekuri and Pal [2007]. We then pursue a deeper study of this linear program and its variations, which leads to an algorithm for the k-person ATSPP (where k s-t paths of minimum total length are sought) and an O(log n)-approximation for the directed latency problem. We present a deterministic (1+√5 2)-approximation algorithm for the s-t path TSP for an arbitrary metric. Given a symmetric metric cost on @math vertices including two prespecified endpoints, the problem is to find a shortest Hamiltonian path between the two endpoints; Hoogeveen showed that the natural variant of Christofides' algorithm is a 5 3-approximation algorithm for this problem, and this asymptotically tight bound in fact had been the best approximation ratio known until now. We modify this algorithm so that it chooses the initial spanning tree based on an optimal solution to the Held-Karp relaxation rather than a minimum spanning tree; we prove this simple but crucial modification leads to an improved approximation ratio, surpassing the 20-year-old barrier set by the natural Christofides' algorithm variant. Our algorithm also proves an upper bound of 1+√5 2 on the integrality gap of the path-variant Held-Karp relaxation. The techniques devised in this paper can be applied to other optimization problems as well: these applications include improved approximation algorithms and improved LP integrality gap upper bounds for the prize-collecting s-t path problem and the unit-weight graphical metric s-t path TSP. Given an arc-weighted directed graph G = (V, A, spl lscr ) and a pair of nodes s, t, we seek to find an s-t walk of length at most B that maximizes some given function f of the set of nodes visited by the walk. The simplest case is when we seek to maximize the number of nodes visited: this is called the orienteering problem. Our main result is a quasi-polynomial time algorithm that yields an O(log OPT) approximation for this problem when f is a given submodular set function. We then extend it to the case when a node v is counted as visited only if the walk reaches v in its time window [R(v), D(v)]. We apply the algorithm to obtain several new results. First, we obtain an O(log OPT) approximation for a generalization of the orienteering problem in which the profit for visiting each node may vary arbitrarily with time. This captures the time window problem considered earlier for which, even in undirected graphs, the best approximation ratio known [Bansal, (2004)] is O(log sup 2 OPT). The second application is an O(log sup 2 k) approximation for the k-TSP problem in directed graphs (satisfying asymmetric triangle inequality). This is the first non-trivial approximation algorithm for this problem. The third application is an O(log sup 2 k) approximation (in quasi-poly time) for the group Steiner problem in undirected graphs where k is the number of groups. This improves earlier ratios (Garg, ) by a logarithmic factor and almost matches the inapproximability threshold on trees (Halperin and Krauthgamer, 2003). This connection to group Steiner trees also enables us to prove that the problem we consider is hard to approximate to a ratio better than spl Omega (log sup 1- spl epsi OPT), even in undirected graphs. Even though our algorithm runs in quasi-poly time, we believe that the implications for the approximability of several basic optimization problems are interesting. The Held-Karp heuristic for the Traveling Salesman Problem (TSP) has in practice provided near-optimal lower bounds on the cost of solutions to the TSP. We analyze the structure of Held-Karp solutions in order to shed light on their quality. In the symmetric case with triangle inequality, we show that a class of instances has planar solutions. We also show that Held-Karp solutions have a certain monotonicity property. This leads to an alternate proof of a result of Wolsey, which shows that the value of Held-Karp heuristic is always at least 2 3 OPT, where OPT is the cost of the optimum TSP tour. Additionally, we show that the value of the Held-Karp heuristic is equal to that of the linear relaxation of the biconnected-graph problem when edge costs are non-negative. In the asymmetric case with triangle inequality, we show that there are many equivalent definitions of the Held-Karp heuristic, which include finding optimally weighted 1-arborescences, 1-antiarborescences, asymmetric 1-trees, and assignment problems. We prove that monotonicity holds in the asymmetric case as well. These theorems imply that the value of the Held-Karp heuristic is no less than OPT and no less than the value of the Balas-Christofides heuristic for the asymmetric TSP. For the 1,2-TSP, we show that the Held-Karp heuristic cannot do any better than 9 10 OPT, even as the number of nodes tends to infinity. Portions of this thesis are joint work with David Shmoys.