14506171
Optimal constrained graph exploration
We address the problem of exploring an unknown graph <i>G</i> = (<i>V</i>, <i>E</i>) from a given start node <i>s</i> with either a tethered robot or a robot with a fuel tank of limited capacity, the former being a tighter constraint. In both variations of the problem, the robot can only move along the edges of the graph, i.e, it cannot jump between non-adjacent vertices. In the tethered robot case, if the tether (rope) has length <i>l</i>, then the robot must remain within distance <i>l</i> from the start node <i>s</i>. In the second variation, a fuel tank of limited capacity forces the robot to return to <i>s</i> after traversing <i>C</i> edges. The efficiency of algorithms for both variations of the problem is measured by the number of edges traversed during the exploration. We present an algorithm for a tethered robot which explores the graph in <i>&Ogr;</i>(¦<i>E</i>¦) edge traversals. The problem of exploration using a robot with a limited fuel tank capacity can be solved with a simple reduction from the tethered robot case and also yields a <i>&Ogr;</i>(¦<i>E</i>¦) algorithm. This improves on the previous best known bound of <i>&Ogr;</i>(¦<i>E</i>¦ + ¦<i>V</i>¦log <sup>2</sup>¦<i>V</i>¦) in [4]. Since the lower bound for the graph exploration problems is ¦<i>E</i>¦, our algorithm is optimal, thus answering the open problem of Awerbuch, Betke, Rivest, and Singh [3].
5564142
Provable algorithms for parallel generalized sweep scheduling
Abstract We present provably efficient parallel algorithms for sweep scheduling, which is a commonly used technique in radiation transport problems, and involves inverting an operator by iteratively sweeping across a mesh from multiple directions. Each sweep involves solving the operator locally at each cell. However, each direction induces a partial order in which this computation can proceed. On a distributed computing system, the goal is to schedule the computation, so that the length of the schedule is minimized. Due to efficiency and coupling considerations, we have an additional constraint, namely, a mesh cell must be processed on the same processor along each direction. Problems similar in nature to sweep scheduling arise in several other applications, and here we formulate a combinatorial generalization of this problem that captures the sweep scheduling constraints,and call it the generalized sweep scheduling problem. Several heuristics have been proposed for this problem; see [S. Pautz, An algorithm for parallel S n sweeps on unstructured meshes, Nucl. Sci. Eng. 140 (2002) 111–136; S. Plimpton, B. Hendrickson, S. Burns, W. McLendon, Parallel algorithms for radiation transport on unstructured grids, Super Comput. (2001)] and the references therein; but none of these have provable worst case performance guarantees. Here we present a simple, almost linear time randomized algorithm for the generalized sweep scheduling problem that (provably) gives a schedule of length at most O ( log 2 n ) times the optimal schedule for instances with n cells, when the communication cost is not considered, and a slight variant, which coupled with a much more careful analysis, gives a schedule of (expected) length O ( log m log log log m ) times the optimal schedule for m processors. These are the first such provable guarantees for this problem. The algorithm can be extended with an additional multiplicative factor in the case when we have inter-processor communication latency, in the models of Rayward-Smith [UET scheduling with inter-processor communication delays, Discrete Appl. Math. 18 (1) (1987) 55–71] and Hwang et al. [Scheduling precedence graphs in systems with inter-processor communication times, SIAM J. Comput. 18(2) (1989) 244–257]. Our algorithms are extremely simple, and use no geometric information about the mesh; therefore, these techniques are likely to be applicable in more general settings. We also design a priority based list schedule using these ideas, with the same theoretical guarantee, but much better performance in practice; combining this algorithm with a simple block decomposition also lowers the overall communication cost significantly. Finally, we perform a detailed experimental analysis of our algorithm. Our results indicate that the algorithm compares favorably with the length of the schedule produced by other natural and efficient parallel algorithms proposed in the literature [S. Pautz, An Algorithm for parallel S n sweeps on unstructured meshes, Nucl. Sci. Eng. 140 (2002) 111–136; S. Plimpton, B. Hendrickson, S. Burns, W. McLendon, Parallel algorithms for radiation transport on unstructured grids, Super Comput. (2001)].
6168494
Approximation algorithms for scheduling on multiple machines
We develop a single rounding algorithm for scheduling on unrelated parallel machines; this algorithm works well with the known linear programming, quadratic programming, and convex programming-relaxations for scheduling to minimize completion time, makespan, and other well-studied objective functions. We obtain the following applications for the general setting of unrelated parallel machines: (i) a bicriteria algorithm for a schedule whose weighted completion-time and makespan simultaneously exhibit the current-best individual approximations for these criteria (3/2 and 2, respectively); (ii) better-than-two approximation guarantees for scheduling under the L/sub p/ norm for all 1 < p < /spl infin/, improving on the 2-approximation algorithms of Azar & Epstein; and (iii) the first constant-factor multicriteria approximation algorithms that handle the weighted completion-time and any given collection of integer L/sub p/ norms. Our algorithm yields a common generalization of rounding theorems due to Karp et al and Shmoys & Tardos; among other applications, this yields an improved approximation for scheduling with resource-dependent processing times studied by Grigoriev et al.
47278894
Equilibria in Topology Control Games for Ad Hoc Networks
We study topology control problems in ad hoc networks where network nodes get to choose their power levels in order to ensure desired connectivity properties. Unlike most other work on this topic, we assume that the network nodes are owned by different entities, whose only goal is to maximize their own utility that they get out of the network without considering the overall performance of the network. Game theory is the appropriate tool to study such selfish nodes: we define several topology control games in which the nodes need to choose power levels in order to connect to other nodes in the network to reach their communication partners while at the same time minimizing their costs. We study Nash equilibria and show that—among the games we define—these can only be guaranteed to exist if each network node is required to be connected to all other nodes (we call this the Strong Connectivity Game). For a variation called Connectivity Game, where each node is only required to be connected (possibly via intermediate nodes) to a given set of nodes, we show that Nash equilibria do not necessarily exist. We further study how to find Nash equilibria with incentive-compatible algorithms and compare the cost of Nash equilibria to the cost of a social optimum, which is a radius assignment that minimizes the total cost in a network where nodes cooperate. We also study variations of the games; one where nodes not only have to be connected, but k-connected, and one that we call the Reachability Game, where nodes have to reach as many other nodes as possible, while keeping costs low. We extend our study of the Strong Connectivity Game and the Connectivity Game to wireless networks with directional antennas and wireline networks, where nodes need to choose neighbors to which they will pay a link. Our work is a first step towards game-theoretic analyses of topology control in wireless and wireline networks.
5797236
Strong edge coloring for channel assignment in wireless radio networks
We give efficient sequential and distributed approximation algorithms for strong edge coloring graphs modeling wireless networks. Strong edge coloring is equivalent to computing a conflict-free assignment of channels or frequencies to pairwise links between transceivers in the network
41766531
High Performance Data Mining
The current decade has seen an explosive growth in database technology and the amount of data collected. This has created an unprecedented opportunity for ”data mining,” which is a process of efficient supervised or unsupervised discovery of interesting information hidden in the data. The focus of this symposium is to bring together experts from the academia and the industry, including reseachers and practitioners, to discuss issues in this important area. Owing to the huge size of data and computation involved in data mining algorithms, parallel processing is often considered an essential component for a successful data mining solution - which will be a special emphasis of the symposium. Talks in this mini-symposium will cover both the current state-of the-practice as well as state-of-the-research in data mining. A highlight of the symposium is a panel discussion on the theory and practice of data mining.
26367203
Control of a single robot in a decentralized multi-robot system
In a decentralized multi-robot system, each robot is controlled by an independent controller and the information obtained by each robot through its proprioceptive sensing devices is not shared with other robots. If the coordination of multiple robots in a cooperative manipulation task is to be accomplished, each robot must be able to exhibit controlled interaction with other robots and objects with minimal information. In this paper, the authors address the problem of maintaining rolling contact between a robot arm and an external, moving object. The authors assume that a nominal model of the motion of the moving object is available. The dynamic system that characterizes the motion of the robot arm and the moving object is acatastatic and nonholonomic. The authors design a nonlinear feedback for such a system that successfully maintains rolling contact. Simulation results are presented to demonstrate the effectiveness of the system.<<ETX>>
206537669
Optimal control of systems with unilateral constraints
Problems in robotics and biomechanics such as trajectory planning or resolution of redundancy can be effectively solved using optimal control. Such systems are often subject to unilateral constraints. Examples include tasks involving contacts (e.g., walking, running, multifingered or multiarm manipulation), and other tasks that may not involve contacts but in which the system state or the inputs must satisfy inequality conditions (e.g., limits on actuator forces). This paper shows how problems of optimal control in robotics that involve unilateral constraints can be efficiently solved by first formulating the constrained optimal control problem as an unconstrained problem of the calculus of variations and then solving it using an integral formulation. This method has several advantages over the Pontryagin minimum principle which is traditionally employed to solve such problems. An example of two-arm manipulation with inequality constraints due to Coulomb friction is used to demonstrate the formulation of the problem and the algorithms.
4677633
A Strategic Modeling Technique for Change Management in Organizations Undergoing BPR
Modern day organizations seek to rationalize, innovate, and adapt to changing environments, and circumstances as part of Business Process Reengineering (BPR) efforts. Irrespective of the process reengineering program selected, and the technique used to model, BPR brings with it the issues of organizational, and process changes, which involves managing organizational changes (also called “change management”). Change management is non-trivial, as organizational changes are difficult to accomplish. Though some attempt has been made to model change management in enterprise information systems using conventional conceptual modeling techniques, they have just addressed “what” a change process is like, and they don’t address “why” the process is the way it is. Our approach is novel in the sense that it presents an actor-dependency-based 5-phased technique for analyzing, and modeling early-phase requirements of change management that provides the motivations, intents, and rationales behind activities.
16578484
On the Complexity of Error Explanation
When a system fails to satisfy its specification, the model checker produces an error trace (or counter-example) that demonstrates an undesirable behavior, which is then used in debugging the system. Error explanation is the task of discovering errors in the system or the reasons why the system exhibits the error trace. While there has been considerable recent interest in automating this task and developing tools based on different heuristics, there has been very little effort in characterizing the computational complexity of the problem of error explanation. 
 
In this paper, we study the complexity of two popular heuristics used in error explanation. The first approach tries to compute the smallest number of system changes that need to be made in order to ensure that the given counter-example is no longer exhibited, with the intuition being that these changes are the errors that need fixing. The second approach relies on the observation that differences between correct and faulty runs of a system shed considerable light on the sources of errors. In this approach, one tries to compute the correct trace of the system that is closest to the counter-example. We consider three commonly used abstractions to model programs and systems, namely, finite state Mealy machines, extended finite state machines and pushdown automata. We show that the first approach of trying to find the fewest program changes is NP-complete no matter which of the three formal models is used to represent the system. Moreover we show that no polynomial factor approximation algorithm for computing the smallest set of changes is possible, unless P = NP. For the second approach, we present a polynomial time algorithm that finds the closest correct trace, when the program is represented by a Mealy machine or a pushdown automata. When the program is represented by an extended finite state machine, the problem is once again NP-complete, and no polynomial factor approximation algorithm is likely.