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30
Complexity and Approximation
, 1999
"... Abstract. In this survey the following model is considered. We assume that an instance I of a computationally hard optimization problem has been solved and that we know the optimum solution of such instance. Then a new instance I ′ is proposed, obtained by means of a slight perturbation of instance ..."
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Cited by 176 (1 self)
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Abstract. In this survey the following model is considered. We assume that an instance I of a computationally hard optimization problem has been solved and that we know the optimum solution of such instance. Then a new instance I ′ is proposed, obtained by means of a slight perturbation of instance I. How can we exploit the knowledge we have on the solution of instance I to compute a (approximate) solution of instance I ′ in an efficient way? This computation model is called reoptimization and is of practical interest in various circumstances. In this article we first discuss what kind of performance we can expect for specific classes of problems and then we present some classical optimization problems (i.e. Max Knapsack, Min Steiner Tree, Scheduling) in which this approach has been fruitfully applied. Subsequently, we address vehicle routing problems and we show how the reoptimization approach can be used to obtain good approximate solution in an efficient way for some of these problems. 1
Geometric Shortest Paths and Network Optimization
 Handbook of Computational Geometry
, 1998
"... Introduction A natural and wellstudied problem in algorithmic graph theory and network optimization is that of computing a "shortest path" between two nodes, s and t, in a graph whose edges have "weights" associated with them, and we consider the "length" of a path to be the sum of the weights of t ..."
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Cited by 147 (12 self)
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Introduction A natural and wellstudied problem in algorithmic graph theory and network optimization is that of computing a "shortest path" between two nodes, s and t, in a graph whose edges have "weights" associated with them, and we consider the "length" of a path to be the sum of the weights of the edges that comprise it. Efficient algorithms are well known for this problem, as briefly summarized below. The shortest path problem takes on a new dimension when considered in a geometric domain. In contrast to graphs, where the encoding of edges is explicit, a geometric instance of a shortest path problem is usually specified by giving geometric objects that implicitly encode the graph and its edge weights. Our goal in devising efficient geometric algorithms is generally to avoid explicit construction of the entire underlying graph, since the full induced graph may be very large (even exponential in the input size, or infinite). Computing an optimal
The Traveling Salesman Problem and Its Variations
, 2002
"... Introduction The Maximum Traveling Salesman Problem (MAX TSP), also known informally as the "taxicab ripoff problem", is stated as follows: Given an n \Theta n real matrix c = (c ij ), called a weight matrix, find a hamiltonian cycle i 1 7! i 2 7! : : : 7! i n 7! i 1 , for which the maximum value ..."
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Cited by 96 (4 self)
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Introduction The Maximum Traveling Salesman Problem (MAX TSP), also known informally as the "taxicab ripoff problem", is stated as follows: Given an n \Theta n real matrix c = (c ij ), called a weight matrix, find a hamiltonian cycle i 1 7! i 2 7! : : : 7! i n 7! i 1 , for which the maximum value of c i 1 i 2 + c i 2 i 3 + : : : + c i n\Gamma1 i n + c i n i 1 is attained. Here (i 1 ; : : : ; i n ) is a permutation of the set f1; : : : ; ng. Of course, in this general setting, the Maximum Traveling Salesman Problem is equivalent to the Minimum Traveling Salesman Problem, Partially supported by NSF Grant DMS 9734138 since the maximum weight hamiltonian cycle with the weight matrix c corresponds to the minimum weight hamiltonian cycle with the weight matrix \Gammac. What makes the MAX TSP special is that there are some interesting and natural special cases of weights c ij , not preserved by the sign reversal, where much more can be said about the problem than in the general case. Be
Sublinear Time Algorithms for Metric Space Problems
"... In this paper we give approximation algorithms for the following problems on metric spaces: Furthest Pair, k median, Minimum Routing Cost Spanning Tree, Multiple Sequence Alignment, Maximum Traveling Salesman Problem, Maximum Spanning Tree and Average Distance. The key property of our algorithms i ..."
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Cited by 80 (2 self)
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In this paper we give approximation algorithms for the following problems on metric spaces: Furthest Pair, k median, Minimum Routing Cost Spanning Tree, Multiple Sequence Alignment, Maximum Traveling Salesman Problem, Maximum Spanning Tree and Average Distance. The key property of our algorithms is that their running time is linear in the number of metric space points. As the full specification o`f an npoint metric space is of size \Theta(n 2 ), the complexity of our algorithms is sublinear with respect to the input size. All previous algorithms (exact or approximate) for the problems we consider have running time\Omega\Gamma n 2 ). We believe that our techniques can be applied to get similar bounds for other problems. 1 Introduction In recent years there has been a dramatic growth of interest in algorithms operating on massive data sets. This poses new challenges for algorithm design, as algorithms quite efficient on small inputs (for example, having quadratic running time) ...
Approximation Algorithms for Asymmetric TSP by Decomposing Directed Regular Multigraphs
 Proc. 44th Annual Symposium on Foundations of Computer Science (FOCS
, 2003
"... A directed multigraph is said to be dregular if the indegree and outdegree of every vertex is exactly d. By Hall’s theorem one can represent such a multigraph as a combination of at most n2 cycle covers each taken with an appropriate multiplicity. We prove that if the dregular multigraph does not ..."
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Cited by 53 (1 self)
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A directed multigraph is said to be dregular if the indegree and outdegree of every vertex is exactly d. By Hall’s theorem one can represent such a multigraph as a combination of at most n2 cycle covers each taken with an appropriate multiplicity. We prove that if the dregular multigraph does not contain more than ⌊d/2 ⌋ copies of any 2cycle then we can find a similar decomposition into n2 pairs of cycle covers where each 2cycle occurs in at most one component of each pair. Our proof is constructive and gives a polynomial algorithm to find such a decomposition. Since our applications only need one such a pair of cycle covers whose weight is at least the average weight of all pairs, we also give an alternative, simpler algorithm to extract a single such pair. This combinatorial theorem then comes handy in rounding a fractional solution of an LP relaxation of the maximum Traveling Salesman Problem (TSP) problem. The first stage of the rounding procedure obtains 2cycle covers that do not share a 2cycle with weight at least twice the weight of the optimal solution. Then we show how to extract a tour from the 2 cycle covers, whose weight is at least 2/3 of the weight of the longest tour. This improves upon the previous
Rotation of Periodic Strings and Short Superstrings
, 1996
"... This paper presents two simple approximation algorithms for the shortest superstring problem, with approximation ratios 2 2 3 ( 2:67) and 2 25 42 ( 2:596), improving the best previously published 2 3 4 approximation. The framework of our improved algorithms is similar to that of previous a ..."
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Cited by 26 (0 self)
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This paper presents two simple approximation algorithms for the shortest superstring problem, with approximation ratios 2 2 3 ( 2:67) and 2 25 42 ( 2:596), improving the best previously published 2 3 4 approximation. The framework of our improved algorithms is similar to that of previous algorithms in the sense that they construct a superstring by computing some optimal cycle covers on the distance graph of the given strings, and then break and merge the cycles to finally obtain a Hamiltonian path, but we make use of new bounds on the overlap between two strings. We prove that for each periodic semiinfinite string ff = a1a2 \Delta \Delta \Delta of period q, there exists an integer k, such that for any (finite) string s of period p which is inequivalent to ff, the overlap between s and the rotation ff[k] = ak ak+1 \Delta \Delta \Delta is at most p+ 1 2 q. Moreover, if p q, then the overlap between s and ff[k] is not larger than 2 3 (p+q). In the previous shortes...
Computing cycle covers without short cycles
 In Proc. 9th Ann. European Symp. on Algorithms (ESA)
, 2001
"... A cycle cover of a graph is a spanning subgraph where each node is part of exactly one simple cycle. A kcycle cover is a cycle cover where each cycle has length at least k. We call the decision problems whether a directed or undirected graph has a kcycle cover kDCC and kUCC. Given a graph with e ..."
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Cited by 20 (5 self)
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A cycle cover of a graph is a spanning subgraph where each node is part of exactly one simple cycle. A kcycle cover is a cycle cover where each cycle has length at least k. We call the decision problems whether a directed or undirected graph has a kcycle cover kDCC and kUCC. Given a graph with edge weights one and two, MinkDCC and MinkUCC are the minimization problems of finding a kcycle cover with minimum weight. We present factor 4=3 approximation algorithms for MinkDCC with running time O(n
A 2 2/3Approximation Algorithms for the Shortest Superstring Problem
 DIMACS WORKSHOP ON SEQUENCING AND MAPPING
, 1995
"... Given a collection of strings S = fs1; : : : ; sng over an alphabet, a superstring of S is a string containing each si as a substring; that is, for each i, 1 i n, contains a block of jsij consecutive characters that match si exactly. The shortest superstring problem is the problem of nding a superst ..."
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Cited by 14 (0 self)
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Given a collection of strings S = fs1; : : : ; sng over an alphabet, a superstring of S is a string containing each si as a substring; that is, for each i, 1 i n, contains a block of jsij consecutive characters that match si exactly. The shortest superstring problem is the problem of nding a superstring of minimum length. The shortest superstring problem has applications in both data compression and computational biology. In data compression, the problem is a part of a general model of string compression proposed by Gallant, Maier and Storer (JCSS '80). Much of the recent interest in the problem is due to its application to DNA sequence assembly. The problem has been shown to be NPhard; in fact, it was shown by Blum et al.(JACM '94) to be MAX SNPhard. The rst O(1)approximation was also due to Blum et al., who gave an algorithm that always returns a superstring no more than 3 times the length of an optimal solution. Several researchers have published results that improve on the approximation ratio; of these, the best previous result is our algorithm ShortString, which achieves a 2 3
A 3/4approximation algorithm for maximum ATSP with weights zero and one
 Proc. of the 7th Int. Workshop on Approximation Algorithms for Combinatorial Optimization Problems (APPROX), volume 3122 of Lecture Notes in Computer Science
, 2004
"... We present a polynomial time 3/4approximation algorithm for the maximum asymmetric TSP with weights zero and one. As applications, we get a 5/4approximation algorithm for the (minimum) asymmetric TSP with weights one and two and a 3/4approximation algorithm for the Maximum Directed Path Packing ..."
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Cited by 12 (0 self)
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We present a polynomial time 3/4approximation algorithm for the maximum asymmetric TSP with weights zero and one. As applications, we get a 5/4approximation algorithm for the (minimum) asymmetric TSP with weights one and two and a 3/4approximation algorithm for the Maximum Directed Path Packing Problem.
zapproximations
 Journal of Algorithms
, 2001
"... Approximation algorithms for NPhard optimization problems have been widely studied for over three decades. Most of these measure the quality of the solution produced by taking the ratio of the cost of the solution produced by the algorithm to the cost of an optimal solution. In certain cases, this ..."
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Cited by 12 (4 self)
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Approximation algorithms for NPhard optimization problems have been widely studied for over three decades. Most of these measure the quality of the solution produced by taking the ratio of the cost of the solution produced by the algorithm to the cost of an optimal solution. In certain cases, this ratio may not be very meaningful for example, if the ratio of the worst solution to the best solution is at most some constant ff, then an approximation algorithm with factor ff may in fact yield the worst solution! To overcome this hurdle (among others), several authors have independently suggested the use of a different measure which we call zapproximation. An algorithm is an ff zapproximation if it runs in polynomial time, and produces a solution whose distance from the optimal one is at most ff times the distance between the optimal solution and the worst possible solution. The results known so far about zapproximations are either of the inapproximability type or rather straightforward observations. We design polynomial time algorithms for several fundamental discrete optimization problems, in particular we obtain a zapproximation factor of 1 2 for the directed traveling salesman problem (TSP) (with no triangle inequality assumption). For the undirected TSP this improves to