The Maximum Traveling Salesman Problem with Submodular Rewards

In this paper, we look at the problem of finding the tour of maximum reward on an undirected graph where the reward is a submodular function, that has a curvature of $\kappa$, of the edges in the tour. This problem is known to be NP-hard. We analyze two simple algorithms for finding an approximate solution. Both algorithms require $O(|V|^3)$ oracle calls to the submodular function. The approximation factors are shown to be $\frac{1}{2+\kappa}$ and $\max\set{\frac{2}{3(2+\kappa)},2/3(1-\kappa)}$, respectively; so the second method has better bounds for low values of $\kappa$. We also look at how these algorithms perform for a directed graph and investigate a method to consider edge costs in addition to rewards. The problem has direct applications in monitoring an environment using autonomous mobile sensors where the sensing reward depends on the path taken. We provide simulation results to empirically evaluate the performance of the algorithms.Comment: Extended version of ACC 2013 submission (including p-system greedy bound with curvature

A 7/9 - Approximation Algorithm for the Maximum Traveling Salesman Problem

We give a 7/9 - Approximation Algorithm for the Maximum Traveling Salesman Problem.Comment: 6 figure

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 the Extended TSP Problem

We initiate the theoretical study of Ext-TSP, a problem that originates in the area of profile-guided binary optimization. Given a graph $G=(V, E)$ with positive edge weights $w: E \rightarrow R^+$, and a non-increasing discount function $f(\cdot)$ such that $f(1) = 1$ and $f(i) = 0$ for $i > k$, for some parameter $k$ that is part of the problem definition. The problem is to sequence the vertices $V$ so as to maximize $\sum_{(u, v) \in E} f(|d_u - d_v|)\cdot w(u,v)$, where $d_v \in \{1, \ldots, |V| \}$ is the position of vertex~$v$ in the sequence. We show that \prob{Ext-TSP} is APX-hard to approximate in general and we give a $(k+1)$-approximation algorithm for general graphs and a PTAS for some sparse graph classes such as planar or treewidth-bounded graphs. Interestingly, the problem remains challenging even on very simple graph classes; indeed, there is no exact $n^{o(k)}$ time algorithm for trees unless the ETH fails. We complement this negative result with an exact $n^{O(k)}$ time algorithm for trees.Comment: 17 page

Informative Path Planning and Sensor Scheduling for Persistent Monitoring Tasks

In this thesis we consider two combinatorial optimization problems that relate to the field of persistent monitoring. In the first part, we extend the classic problem of finding the maximum weight Hamiltonian cycle in a graph to the case where the objective is a submodular function of the edges. We consider a greedy algorithm and a 2-matching based algorithm, and we show that they have approximation factors of 1/2+κ and max{2/(3(2+κ)),(2/3)(1-κ)} respectively, where κ is the curvature of the submodular function. Both algorithms require a number of calls to the submodular function that is cubic to the number of vertices in the graph. We then present a method to solve a multi-objective optimization consisting of both additive edge costs and submodular edge rewards. We provide simulation results to empirically evaluate the performance of the algorithms. Finally, we demonstrate an application in monitoring an environment using an autonomous mobile sensor, where the sensing reward is related to the entropy reduction of a given a set of measurements. In the second part, we study the problem of selecting sensors to obtain the most accurate state estimate of a linear system. The estimator is taken to be a Kalman filter and we attempt to optimize the a posteriori error covariance. For a finite time horizon, we show that, under certain restrictive conditions, the problem can be phrased as a submodular function optimization and that a greedy approach yields a 1-1/(e^(1-1/e))-approximation. Next, for an infinite time horizon, we characterize the exact conditions for the existence of a schedule with bounded estimation error covariance. We then present a scheduling algorithm that guarantees that the error covariance will be bounded and that the error will die out exponentially for any detectable LTI system. Simulations are provided to compare the performance of the algorithm against other known techniques