In this paper, we propose an approximate projected consensus algorithm for a network to cooperatively compute the intersection of convex sets. Instead of assuming the exact convex projection proposed in the literature, we allow each node to compute an approximate projection and communicate it to its neighbors. The communication graph is directed and time-varying. Nodes update their states by we...

We study the problem of exploring an unknown, strongly connected directed graph. Starting at some node of the graph, we must visit every edge and every node at least once. The goal is to minimize the number of edge traversals. It is known that the competitive ratio of online algorithms for this problem depends on the deficiency d of the graph, which is the minimum number of edges that must be a...

We study existing parallel algorithms for the decomposition of a partitioned graph into its strongly connected components. In particular, we identify several individual procedures that the algorithms are assembled from and show how to assemble a new and more efficient algorithm, called Recursive OBF, to solve the decomposition problem. We also report on a thorough experimental study to evaluate...

Lower Bound. Adrian Vetta Abstract We present a 32 -approximation algorithm for the problem of nding a minimum strongly connected spanning subgraph in a given directed graph. As a corollary we obtain a 3 2 -approximation algorithm for the more general minimum equivalent digraph problem. The performance of our algorithm is measured against a lower bound obtained from a simple matching problem. T...

Lambda lifting is a technique for transforming a program with local function definitions into a program consisting only of global function definitions. The best known lambda lifting algorithm computes the minimal set of extraneous parameters needed by each function in O(n) steps by solving a system of set equations which are recursive if the functions in the program are mutually recursive. Mutu...

Effectively measuring the similarity among images is a challenging problem in image retrieval tasks due to the difficulty of considering the dataset manifold. This paper presents an unsupervised manifold learning algorithm that takes into account the intrinsic dataset geometry for defining a more effective distance among images. The dataset structure is modeled in terms of a Correlation Graph (...

Detecting strongly connected components (SCCs) in a directed graph is a fundamental graph analysis algorithm that is used in many science and engineering domains. Traditional approaches in parallel SCC detection, however, show limited performance and poor scaling behavior when applied to large real-world graph instances. In this paper, we investigate the shortcomings of the conventional approac...