413 research outputs found
Minimum-weight Cycle Covers and Their Approximability
A cycle cover of a graph is a set of cycles such that every vertex is part of
exactly one cycle. An L-cycle cover is a cycle cover in which the length of
every cycle is in the set L.
We investigate how well L-cycle covers of minimum weight can be approximated.
For undirected graphs, we devise a polynomial-time approximation algorithm that
achieves a constant approximation ratio for all sets L. On the other hand, we
prove that the problem cannot be approximated within a factor of 2-eps for
certain sets L.
For directed graphs, we present a polynomial-time approximation algorithm
that achieves an approximation ratio of O(n), where is the number of
vertices. This is asymptotically optimal: We show that the problem cannot be
approximated within a factor of o(n).
To contrast the results for cycle covers of minimum weight, we show that the
problem of computing L-cycle covers of maximum weight can, at least in
principle, be approximated arbitrarily well.Comment: To appear in the Proceedings of the 33rd Workshop on Graph-Theoretic
Concepts in Computer Science (WG 2007). Minor change
Approximating the Held-Karp Bound for Metric TSP in Nearly Linear Time
We give a nearly linear time randomized approximation scheme for the
Held-Karp bound [Held and Karp, 1970] for metric TSP. Formally, given an
undirected edge-weighted graph on edges and , the
algorithm outputs in time, with high probability, a
-approximation to the Held-Karp bound on the metric TSP instance
induced by the shortest path metric on . The algorithm can also be used to
output a corresponding solution to the Subtour Elimination LP. We substantially
improve upon the running time achieved previously
by Garg and Khandekar. The LP solution can be used to obtain a fast randomized
-approximation for metric TSP which improves
upon the running time of previous implementations of Christofides' algorithm
Combinatorial Optimization
Combinatorial Optimization is an active research area that developed from the rich interaction among many mathematical areas, including combinatorics, graph theory, geometry, optimization, probability, theoretical computer science, and many others. It combines algorithmic and complexity analysis with a mature mathematical foundation and it yields both basic research and applications in manifold areas such as, for example, communications, economics, traffic, network design, VLSI, scheduling, production, computational biology, to name just a few. Through strong inner ties to other mathematical fields it has been contributing to and benefiting from areas such as, for example, discrete and convex geometry, convex and nonlinear optimization, algebraic and topological methods, geometry of numbers, matroids and combinatorics, and mathematical programming. Moreover, with respect to applications and algorithmic complexity, Combinatorial Optimization is an essential link between mathematics, computer science and modern applications in data science, economics, and industry
Алгоритм соединения циклов для метрической задачи коммивояжера на максимум
The traveling salesman problem is an important combinatorial optimization problem that involves finding the optimal path between given vertices. The maximum traveling salesman problem has several practical applications, for example, when compressing arbitrary data and analyzing DNA sequences. Even though maximum traveling salesman problem is less developed than minimum traveling salesman problem, there are effective approximate algorithms for solving this problem. The article presents estimates of the accuracy of the best algorithms for the approximate solution of the metric maximum traveling salesman problem. The paper proposes a new algorithm for the approximate solution of the traveling salesman problem to the maximum, consisting of finding the 2-factor of the extreme weight in each graph, and then applying the operation of the optimal connection of cycles into one Hamiltonian cycle. The computational complexity of the algorithm does not exceed O(|V|3). We present a proof of the theorem that for the metric traveling salesman problem, the maximum accuracy of the algorithm is at least 5/6. The quality of the algorithm was tested on randomly generated cost matrices with the Euclidean metric. An analytical and numerical study of the algorithm for combining cycles allowed us to move the hypothesis about the asymptotic accuracy of the algorithm on the class of metric traveling salesman problems to the maximum.Задача коммивояжера на максимум имеет ряд практических приложений, например, при сжатии произвольных данных и анализе последовательностей ДНК. При том, что задача коммивояжера на максимум является менее разработанной, чем задача коммивояжера на минимум, для ее решения существуют эффективные приближенные алгоритмы. В статье приведены оценки точности лучших на сегодняшний день алгоритмов для приближенного решения метрической задачи коммивояжера на максимум, и предлагается еще один алгоритм приближенного решения задачи коммивояжера на максимум, состоящий из поиска 2-фактора максимального веса в заданном графе, а затем применения операции оптимального соединения циклов в один гамильтонов цикл. Приведено доказательство, что для метрической задачи коммивояжера на максимум отношение длины найденного алгоритмом гамильтонова цикла к максимально возможной длине гамильтонова цикла не менее 5/6. Вычислительная сложность алгоритма не превышает O(|V|3). Проведено тестирование качества алгоритма на случайно сгенерированных матрицах стоимостей с евклидовой метрикой. Аналитическое и численное исследование алгоритма объединения циклов позволило выдвинуть гипотезу об асимптотической точности алгоритма на классе метрических задач коммивояжера на максимум
On a generalization of iterated and randomized rounding
We give a general method for rounding linear programs that combines the
commonly used iterated rounding and randomized rounding techniques. In
particular, we show that whenever iterated rounding can be applied to a problem
with some slack, there is a randomized procedure that returns an integral
solution that satisfies the guarantees of iterated rounding and also has
concentration properties. We use this to give new results for several classic
problems where iterated rounding has been useful
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