Topic
Greedy algorithm
About: Greedy algorithm is a research topic. Over the lifetime, 15347 publications have been published within this topic receiving 393945 citations.
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TL;DR: Given an upper bound on the queue overflow probability, it is shown that the throughput of the queue-length-based policy is a strictly increasing function of N while the throughputof the greedy policy eventually goes to a constant.
Abstract: In this correspondence, we consider a cellular network consisting of a base station and N receivers. The channel states of the receivers are assumed to be identical and independent of each other. The goal is to compare the throughput of two different scheduling policies (a queue-length-based (QLB) policy and a greedy policy) given an upper bound on the queue overflow probability or the delay violation probability. We consider a multistate channel model, where each channel is assumed to be in one of L states. Given an upper bound on the queue overflow probability or an upper bound on the delay violation probability, we show that the total network throughput of the (QLB) policy is no less than the throughput of the greedy policy for all N. We also obtain a lower bound on the throughput of the (QLB) policy. For sufficiently large N, the lower bound is shown to be tight, strictly increasing with N, and strictly larger than the throughput of the greedy policy. Further, for a simple multistate channel model-ON-OFF channel, we prove that the lower bound is tight for all N
81 citations
TL;DR: It is shown that finding a local optimum solution with respect to the Lin–Kernighan heuristic for the traveling salesman problem is PLS-complete, and thus as hard as any local search problem.
Abstract: It is shown that finding a local optimum solution with respect to the Lin–Kernighan heuristic for the traveling salesman problem is PLS-complete, and thus as hard as any local search problem.
81 citations
01 Dec 2015
TL;DR: A partition scheme to partition the sets into several subsets and guarantee that two sets are similar only if they share a common subset, and an adaptive grouping mechanism that can reduce the complexity to O(s log s).
Abstract: We study the exact set similarity join problem, which, given two collections of sets, finds out all the similar set pairs from the collections. Existing methods generally utilize the prefix filter based framework. They generate a prefix for each set and prune all the pairs whose prefixes are disjoint. However the pruning power is limited, because if two dissimilar sets share a common element in their prefixes, they cannot be pruned. To address this problem, we propose a partition-based framework. We design a partition scheme to partition the sets into several subsets and guarantee that two sets are similar only if they share a common subset. To improve the pruning power, we propose a mixture of the subsets and their 1-deletion neighborhoods (the subset of a set by eliminating one element). As there are multiple allocation strategies to generate the mixture, we evaluate different allocations and design a dynamic-programming algorithm to select the optimal one. However the time complexity of generating the optimal one is O(s3) for a set with size s. To speed up the allocation selection, we develop a greedy algorithm with an approximation ratio of 2. To further reduce the complexity, we design an adaptive grouping mechanism, and the two techniques can reduce the complexity to O(s log s). Experimental results on three real-world datasets show our method achieves high performance and outperforms state-of-the-art methods by 2-5 times.
80 citations
TL;DR: This work proposes two different Mixed Integer Linear Programming formulations based on extensions of Support Vector Machines to overcome shortcomings in feature selection and results obtained better predictions with consistently fewer relevant features.
80 citations
TL;DR: It is observed that consistency should be weighted more heavily than coverage, presumably because a lack of coverage can be corrected by learning additional rules, and the potential of using metalearning for obtaining alternative rule learning heuristics is investigated.
Abstract: The primary goal of the research reported in this paper is to identify what criteria are responsible for the good performance of a heuristic rule evaluation function in a greedy top-down covering algorithm. We first argue that search heuristics for inductive rule learning algorithms typically trade off consistency and coverage, and we investigate this trade-off by determining optimal parameter settings for five different parametrized heuristics. In order to avoid biasing our study by known functional families, we also investigate the potential of using metalearning for obtaining alternative rule learning heuristics. The key results of this experimental study are not only practical default values for commonly used heuristics and a broad comparative evaluation of known and novel rule learning heuristics, but we also gain theoretical insights into factors that are responsible for a good performance. For example, we observe that consistency should be weighted more heavily than coverage, presumably because a lack of coverage can later be corrected by learning additional rules.
80 citations