One significant problem in exchange networks is finding the equilibrium. To solve this problem, the concept of stable outcome has been developed. However, there are few effective methods to solve it from the point of ...
详细信息
ISBN:
(纸本)9781538647271
One significant problem in exchange networks is finding the equilibrium. To solve this problem, the concept of stable outcome has been developed. However, there are few effective methods to solve it from the point of graph theory. In this paper, we propose a minimum cost stable outcome (MCSO) problem, which is to find a stable outcome whose total transaction cost is minimized Two algorithms have been designed to solve this problem on unit and general profit networks respectively. For unit profit networks, we use minimum cost edge cover based method to give the optimal solution. For general profit networks, we develop an approximate algorithm and prove that performance ratio is no more than twice the optimal value. Moreover, we provide the probabilistic analysis. At last, extensive experiments have been conducted on synthetic and real-life datasets. Experimental results validate the performance of the proposed algorithms.
In this paper, the problem about optimal iterative learning control of general nonlinear discrete-time systems has been studied. Based on the sufficient conditions of the existence of optimal iterative learning contro...
详细信息
ISBN:
(纸本)9783037852118
In this paper, the problem about optimal iterative learning control of general nonlinear discrete-time systems has been studied. Based on the sufficient conditions of the existence of optimal iterative learning control in general nonlinear discrete-time systems, in view of the practical application, propose an approximate iterative algorithm, and prove the approximate iterative control restraining to the optimum control.
In this paper, the problem about optimal iterative learning control of general nonlinear discrete-time systems has been studied. Based on the sufficient conditions of the existence of optimal iterative learning contro...
详细信息
ISBN:
(数字)9783642253492
ISBN:
(纸本)9783642253485
In this paper, the problem about optimal iterative learning control of general nonlinear discrete-time systems has been studied. Based on the sufficient conditions of the existence of optimal iterative learning control in general nonlinear discrete-time systems, in view of the practical application, propose an approximate iterative algorithm, and prove the approximate iterative control restraining to the optimum control.
In this paper we propose and study the problem of k-Collective influential facility placement over moving object. Specifically, given a set of candidate locations, a group of moving objects, each of which is associate...
详细信息
ISBN:
(纸本)9781728133638
In this paper we propose and study the problem of k-Collective influential facility placement over moving object. Specifically, given a set of candidate locations, a group of moving objects, each of which is associated with a collection of reference points, as well as a budget k, we aim to mine a group of k locations, the combination of whom can influence the most number of moving objects. We show that this problem is NP-hard and present a basic hill-climb algorithm, namely GreedyP. We prove this method with (1 - 1/e) approximation ratio. One core challenge is to identify and reduce the overlap of the influence from different selected locations to maximize the marginal benefits. Therefore, the GreedyP approach may be very costly when the number of moving objects is large. In order to address the problem, we also propose another GreedyPS algorithm based on FM-sketch technique, which maps the moving objects to bitmaps such that the marginal benefit can be easily observed through bit-wise operations. Through this way, we are able to save more than a half running time while preserving the result quality. Experiments on real datasets verify the efficiency and effectiveness for both algorithms we propose in this paper.
Reorganizing bus frequency to cater for the actual travel demand can save the cost of the public transport system significantly. Many, if not all, existing studies formulate this as a bus frequency optimization proble...
详细信息
ISBN:
(纸本)9783030594152;9783030594169
Reorganizing bus frequency to cater for the actual travel demand can save the cost of the public transport system significantly. Many, if not all, existing studies formulate this as a bus frequency optimization problem which tries to minimize passengers' average waiting time. However, many investigations have confirmed that the user satisfaction drops faster as the waiting time increases. Consequently, this paper studies the bus frequency optimization problem considering the user satisfaction. Specifically, for the first time to our best knowledge, we study how to schedule the buses such that the total number of passengers who could receive their bus services within the waiting time threshold is maximized. We prove that this problem is NP-hard, and present an index-based algorithm with (1 - 1/e) approximation ratio. By exploiting the locality property of routes in a bus network, we propose a partition-based greedy method which achieves a (1 - rho)(1 - 1/e) approximation ratio. Then we propose a progressive partition-based greedy method to further improve the efficiency while achieving a (1 - rho)(1 - 1/e - epsilon) approximation ratio. Experiments on a real city-wide bus dataset in Singapore verify the efficiency, effectiveness, and scalability of our methods.
Foreground detection in complex scenarios is a challenging task. In this work, we propose to detect foreground by incrementally learning a cross-covariance based subspace. In our method, we first introduce the cross-c...
详细信息
ISBN:
(纸本)9781509064151;9781509064144
Foreground detection in complex scenarios is a challenging task. In this work, we propose to detect foreground by incrementally learning a cross-covariance based subspace. In our method, we first introduce the cross-covariance based two dimensional principal component analysis(2 DPCA) algorithm into foreground detection field for better background ***, we extend the conventional cross-covariance based 2 DPCA algorithm into an incremental one, which helps to model background in an adaptive way. Moreover, we consider the sparse and the continuous characteristics of the foreground, and formulate them as a fused lasso problem. By adding the fused lasso regularization into the proposed subspace learning process,we integrate the background recovery and the foreground estimation into a single optimization framework. Finally, we design an efficient approximate algorithm which solves the optimization problem effectively. We compare our method with the state of the art methods on multiple challenging video sequences. The experimental results demonstrate the effectiveness and the advantages of the proposed method.
Federated learning, a new distributed learning paradigm, has the advantage of sharing model information without revealing data privacy. However, considering the selfishness of organizations, they will not participate ...
详细信息
ISBN:
(数字)9789819708086
ISBN:
(纸本)9789819708079;9789819708086
Federated learning, a new distributed learning paradigm, has the advantage of sharing model information without revealing data privacy. However, considering the selfishness of organizations, they will not participate in federated learning without compensation. To address this problem, in this paper, we design a feature importance-aware vertical federated learning incentive mechanism. We first synthesize a small amount of data locally using the interpolation method at the organization and send it to the coordinator for evaluating the contribution of each feature to the learning task. Then, the coordinator calculates the importance value of each feature in the dataset for the current task using the Shapley value method according to the synthetic data. Next, we formulate the process of organization participation in the federation as a feature importance maximization problem based on reverse auction which is a knapsack auction problem. Finally, we design an approximate algorithm to solve the proposed optimization problem and the solution of the approximation algorithm is shown to be 1/2 -approximate to the optimal solution. Furthermore, we prove that the proposed mechanism is truthfulness, individual rationality, and computational efficiency. The superiority of our proposed mechanism is verified through experiments on real-world datasets.
Recently, multipaths solutions have been proposed to improve the quality-of-service (QoS) in communication networks (CN). Peng and Shen algorithm (PSA) was proposed to generate lambda DP/DC - the maximum edge-disjoint...
详细信息
ISBN:
(纸本)9781424440009
Recently, multipaths solutions have been proposed to improve the quality-of-service (QoS) in communication networks (CN). Peng and Shen algorithm (PSA) was proposed to generate lambda DP/DC - the maximum edge-disjoint-path-set with minimal cost subject to a delay constraint, for lambda >= 2. This paper introduces a different and equally important problem, lambda DP/DR, to obtain the maximal edge-disjoint-path-set with maximum reliability subject to a given delay constraint, for lambda >= 1. lambda DP/DR is applicable to time critical applications that require non-compromised time delay while demanding maximum system reliability. In this paper we show how lambda DP/DR is different from lambda DP/DC, and propose an approximate algorithm similar to the Lagrange-relaxation based PSA to solve the problem. Our simulations on three randomly generated CNs show that our polynomial time algorithm produced lambda DP/DR with comparable optimality to that obtained using the NP-hard brute-force approach.
An important index widely used to analyze social and information networks is betweenness centrality. In this paper, given a dynamic and directed graph G and a vertex r in G, we present the DyBED algorithm that updates...
详细信息
ISBN:
(纸本)9781538650356
An important index widely used to analyze social and information networks is betweenness centrality. In this paper, given a dynamic and directed graph G and a vertex r in G, we present the DyBED algorithm that updates the (approximate) betweenness centrality of r, when an update operation (vertex/edge insertion/deletion) occurs in G. Our algorithm first during pre-processing computes two subsets of the vertex set of G, called RF(r) and RT (r). The Cartesian product of these two sets defines the sample space of our algorithm. In other words, each sample is a pair, whose first element belongs to RF(r) and second element belongs to RT (r). Then after each update operation, DyBED updates the sets RF(r) and RT (r), the sampled pairs, the information stored for each sample and accordingly, the betweenness centrality of r. We theoretically and empirically evaluate DyBED and show that it yields significant improvement over existing work. In particular, our extensive experiments reveal that DyBED is orders of magnitude faster than most efficient existing algorithms.
In this paper, we consider the rank aggregation problem for information retrieval over Web making use of a kind of metric, the coherence, which considers both the normalized Kendall-tau distance and the size of overla...
详细信息
ISBN:
(纸本)9783642145520
In this paper, we consider the rank aggregation problem for information retrieval over Web making use of a kind of metric, the coherence, which considers both the normalized Kendall-tau distance and the size of overlap between two partial rankings. In general, the top-d coherence aggregation problem is defined as: given collection of partial rankings Pi = {tau(1), tau(2), ... , tau(K)} how to find a final ranking pi with specific length d, which maximizes the total coherence Phi(pi, Pi) = Sigma(K)(i=1) Phi(pi, tau(i)) The corresponding complexity and algorithmic issues are discussed in this paper. Our main technical contribution is a polynomial time approximation scheme (PTAS) for a restricted top-d coherence aggregation problem.
暂无评论