Greedy algorithm

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

A simple polynomial-time rescaling algorithm for solving linear programs

Abstract: The perceptron algorithm, developed mainly in the machine learning literature, is a simple greedy method for finding a feasible solution to a linear program (alternatively, for learning a threshold function). In spite of its exponential worst-case complexity, it is often quite useful, in part due to its noise-tolerance and also its overall simplicity. In this paper, we show that a randomized version of the perceptron algorithm along with periodic rescaling runs in polynomial-time. The resulting algorithm for linear programming has an elementary description and analysis.

Optimizing Segmentation Strategies for Simultaneous Speech Translation

Abstract: In this paper, we propose new algorithms for learning segmentation strategies for simultaneous speech translation. In contrast to previously proposed heuristic methods, our method finds a segmentation that directly maximizes the performance of the machine translation system. We describe two methods based on greedy search and dynamic programming that search for the optimal segmentation strategy. An experimental evaluation finds that our algorithm is able to segment the input two to three times more frequently than conventional methods in terms of number of words, while maintaining the same score of automatic evaluation. 1

Optimal scheduling for streaming of scalable media

Abstract: Scalable, or layered, media representation appears to be more suitable for transmission over the current heterogeneous networks. In this paper we study the problem of scalable layered streaming media delivery over a lossy channel. The goal is to find an optimal transmission policy to achieve the best playback quality at the client end. The problem involves some trade-offs such as time-constrained delivery and data dependencies. For example, a layer should be dropped before transmission if it already has a delay such that it cannot be played before its scheduled time. Moreover, less important layers with near-playback-time may also be dropped or delayed for delivery in order to save bandwidth for other layers with a high priority. We propose a framework for scalable streaming media delivery, that involves a novel scheduling algorithm called Expected runtime Distortion Based Scheduling (EDBS) which decides the order in which packets should be transmitted in order to improve client playback quality in the presence of channel losses. A fast greedy search algorithm is presented that achieves almost the same performance as an exhaustive search technique (98% of the time it results in the same schedule) with very low complexity and is applicable for real-time application.

The pipelined set cover problem

Abstract: A classical problem in query optimization is to find the optimal ordering of a set of possibly correlated selections. We provide an abstraction of this problem as a generalization of set cover called pipelined set cover, where the sets are applied sequentially to the elements to be covered and the elements covered at each stage are discarded. We show that several natural heuristics for this NP-hard problem, such as the greedy set-cover heuristic and a local-search heuristic, can be analyzed using a linear-programming framework. These heuristics lead to efficient algorithms for pipelined set cover that can be applied to order possibly correlated selections in conventional database systems as well as data-stream processing systems. We use our linear-programming framework to show that the greedy and local-search algorithms are 4-approximations for pipelined set cover. We extend our analysis to minimize the lp-norm of the costs paid by the sets, where p ≥ 2 is an integer, to examine the improvement in performance when the total cost has increasing contribution from initial sets in the pipeline. Finally, we consider the online version of pipelined set cover and present a competitive algorithm with a logarithmic performance guarantee. Our analysis framework may be applicable to other problems in query optimization where it is important to account for correlations.