Topic
Greedy algorithm
About: Greedy algorithm is a research topic. Over the lifetime, 15347 publications have been published within this topic receiving 393945 citations.
Papers published on a yearly basis
Papers
More filters
01 Jan 2005
TL;DR: The proposed algorithms are shown to achieve the asymptotic sum-capacity of MIMO downlink channels as the number of users goes to infinity, and clique search based ZFBF is an appealing strategy in MIMo downlink systems with a large number of Users.
Abstract: We consider a multi-user MIMO downlink system employing zero-forcing beamforming (ZFBF) as a spatial multiplexing strategy, and propose low-complexity user subset selection methods based on a clique (fully connected subgraph) search. The proposed algorithms, maximum weighted clique (MWC)-ZFBF and greedy weighted clique (GWC)-ZFBF, are shown to achieve the asymptotic sum-capacity of MIMO downlink channels as the number of users goes to infinity. Thus, clique search based ZFBF is an appealing strategy in MIMO downlink systems with a large number of users.
84 citations
TL;DR: A new bit-loading algorithm for discrete multitone systems that converges faster to the same bit allocation as the optimal discrete bit-filling and bit-removal methods is presented.
Abstract: This letter presents a new bit-loading algorithm for discrete multitone systems that converges faster to the same bit allocation as the optimal discrete bit-filling and bit-removal methods. The algorithm exploits the differences between the subchannel gain-to-noise ratios in order to determine an initial bit allocation and then performs a multiple-bits loading procedure for achieving the requested target rate. Numerical results using asymmetric digital subscriber test loops demonstrate the computational efficiency of the proposed algorithm.
84 citations
TL;DR: The Hopfield network dynamics are modified to allow it to be competitive with other metaheuristics by incorporating controlled stochasticities, making it possible to implement the network in hardware.
84 citations
Proceedings Article•
01 Dec 1997TL;DR: In this paper, the problem of learning how to order, given feedback in the form of preference judgments, is considered. But the problem is NP-complete, even under very restrictive assumptions, and a simple greedy algorithm is guaranteed to find a good approximation.
Abstract: There are many applications in which it is desirable to order rather than classify instances. Here we consider the problem of learning how to order, given feedback in the form of preference judgments, i.e., statements to the effect that one instance should be ranked ahead of another. We outline a two-stage approach in which one first learns by conventional means a preference Junction, of the form PREF(u, v), which indicates whether it is advisable to rank u before v. New instances are then ordered so as to maximize agreements with the learned preference function. We show that the problem of finding the ordering that agrees best with a preference function is NP-complete, even under very restrictive assumptions. Nevertheless, we describe a simple greedy algorithm that is guaranteed to find a good approximation. We then discuss an on-line learning algorithm, based on the "Hedge" algorithm, for finding a good linear combination of ranking "experts." We use the ordering algorithm combined with the on-line learning algorithm to find a combination of "search experts," each of which is a domain-specific query expansion strategy for a WWW search engine, and present experimental results that demonstrate the merits of our approach.
83 citations
TL;DR: The core algorithm used in an implementation of a scheduler currently being installed in a major Asian railway is described, which extends previous work on a greedy heuristic for scheduling trains to provide a powerful and practically useful method that is fast enough for real-time use in many cases.
Abstract: This paper describes the core algorithm used in an implementation of a scheduler currently being installed in a major Asian railway. It extends previous work on a greedy heuristic for scheduling trains, to provide a powerful and practically useful method that is fast enough for real-time use in many cases. Real-world railway systems have constraints that do not fit easily into a simple mathematical formulation. The algorithm described here makes it straightforward to incorporate many such realistic features.
83 citations