Greedy algorithm

About: Greedy algorithm is a research topic. Over the lifetime, 15347 publications have been published within this topic receiving 393945 citations.

A distributed algorithm for the dead end problem of location based routing in sensor networks

Abstract: The dead end problem in greedy forwarding is an important issue of location based routing in sensor networks. It occurs when a message falls into a local minimum using greedy forwarding. Current solutions to this problem are insufficient in either eliminating traffic/path memorization or finding satisfactory short paths. In this paper, we propose a novel algorithm, called partial-partition avoiding geographic routing (PAGER), to solve the problem. The basic idea of PAGER is to divide a sensor network graph into functional subgraphs and provide each sensor node with message forwarding directions based on these subgraphs. PAGER results in loop free short paths without memorization of traffics/paths in sensor nodes. It does not require planarization of the underlying network graph. Further, the mobility adaptability of PAGER makes it suitable for use in mobile sensor networks with frequent topology changes. We implement the PAGER algorithm in two protocols and evaluate them in sensor networks with different parameters. Experimental results show the advantage of PAGER in the context of sensor networks.

“Lattice Cut” - Constructing superpixels using layer constraints

Abstract: Unsupervised over-segmentation of an image into super-pixels is a common preprocessing step for image parsing algorithms. Superpixels are used as both regions of support for feature vectors and as a starting point for the final segmentation. Recent algorithms that construct superpixels that conform to a regular grid (or superpixel lattice) have used greedy solutions. In this paper we show that we can construct a globally optimal solution in either the horizontal or vertical direction using a single graph cut. The solution takes into account both edges in the image, and the coherence of the resulting superpixel regions. We show that our method outperforms existing algorithms for computing superpixel lattices. Additionally, we show that performance can be comparable or better than other contemporary segmentation algorithms which are not constrained to produce a lattice.

Optimal Replenishment and Rework with Multiple Unreliable Supply Sources

Abstract: We study a production-inventory system with multiple unreliable supply sources. Through inspection and rework, the system can improve the quality of the units received from the supply sources. There are two interleaved decisions: the replenishment quantities from the sources and the inspection-rework quantities among the units received. We show the optimal solution to the replenishment decision can be efficiently derived from a greedy algorithm, and inspection-rework is optimally applied to a single source identified by the algorithm. Furthermore, in the case of linear cost functions, it is optimal to place orders from two supply sources, i.e., dual sourcing. The results extend to the infinite-horizon case, where an order-up-to policy is optimal. The model also readily adapts to situations in which the supply imperfection takes the form of a reduced delivery quantity (yield loss).

Enforcing customizable consistency properties in software-defined networks

Abstract: It is critical to ensure that network policy remains consistent during state transitions. However, existing techniques impose a high cost in update delay, and/or FIB space. We propose the Customizable Consistency Generator (CCG), a fast and generic framework to support customizable consistency policies during network updates. CCG effectively reduces the task of synthesizing an update plan under the constraint of a given consistency policy to a verification problem, by checking whether an update can safely be installed in the network at a particular time, and greedily processing network state transitions to heuristically minimize transition delay. We show a large class of consistency policies are guaranteed by this greedy heuristic alone; in addition, CCG makes judicious use of existing heavier-weight network update mechanisms to provide guarantees when necessary. As such, CCG nearly achieves the "best of both worlds": the efficiency of simply passing through updates in most cases, with the consistency guarantees of more heavyweight techniques. Mininet and physical testbed evaluations demonstrate CCG's capability to achieve various types of consistency, such as path and bandwidth properties, with zero switch memory overhead and up to a 3× delay reduction compared to previous solutions.