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: A novel resource provisioning mechanism and a workflow scheduling algorithm, named Greedy Resource Provisioning and modified HEFT (GRP-HEFT), for minimizing the makespan of a given workflow subject to a budget constraint for the hourly-based cost model of modern IaaS clouds.
Abstract: In Infrastructure as a Service (IaaS) Clouds, users are charged to utilize cloud services according to a pay-per-use model. If users intend to run their workflow applications on cloud resources within a specific budget, they have to adjust their demands for cloud resources with respect to this budget. Although several scheduling approaches have introduced solutions to optimize the makespan of workflows on a set of heterogeneous IaaS cloud resources within a certain budget, the hourly-based cost model of some well-known cloud providers (e.g., Amazon EC2 Cloud) can easily lead to a higher makespan and some schedulers may not find any feasible solution. In this article, we propose a novel resource provisioning mechanism and a workflow scheduling algorithm, named Greedy Resource Provisioning and modified HEFT (GRP-HEFT), for minimizing the makespan of a given workflow subject to a budget constraint for the hourly-based cost model of modern IaaS clouds. As a resource provisioning mechanism, we propose a greedy algorithm which lists the instance types according to their efficiency rate. For our scheduler, we modified the HEFT algorithm to consider a budget limit. GRP-HEFT is compared against state-of-the-art workflow scheduling techniques, including MOACS (Multi-Objective Ant Colony System), PSO (Particle Swarm Optimization), and GA (Genetic Algorithm). The experimental results demonstrate that GRP-HEFT outperforms GA, PSO, and MOACS for several well-known scientific workflow applications for different problem sizes on average by 13.64, 19.77, and 11.69 percent, respectively. Also in terms of time complexity, GRP-HEFT outperforms GA, PSO and MOACS.
95 citations
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22 Jan 1995TL;DR: The average-case behavior of the Zero?One Knapsack problem is considered, as well as an on-line version studied by Marchetti-Spaccamela and Vercellis, and it is shown that on average the cost of being forced to give answers on-lines is quite small compared to the optimum solution.
Abstract: We consider the average-case behavior of the Zero?One Knapsack problem, as well as an on-line version studied by Marchetti-Spaccamela and Vercellis. We allow the capacity of the knapsack to grow proportionally to the number of items, so that the optimum solution tends to be ?(n). Under fairly general conditions on the distribution, we obtain a description of the expected value of the optimum offline solution which is accurate up to terms which areo(1). We then consider a simple greedy method for the on-line problem, which is calledOnLineGreedyand is allowed to use knowledge of the distribution, and we show that the solution obtained by this algorithm differs from the true optimum by an average of ?(logn); in fact, we can determine the multiplicative constant hidden by the ?-notation. Thus on average the cost of being forced to give answers on-line is quite small compared to the optimum solution. We also show that no on-line algorithm can improve overOnLineGreedyby more thano(logn). Our results hold, under fairly general conditions on the input distribution, for either the Zero?One Knapsack problem or its linear programming relaxation.
95 citations
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TL;DR: Reduced basis methods for parameter dependent transport dominated problems that are rigorously proven to exhibit rate-optimal perfor- mance when compared with the Kolmogorov n-widths of the solution sets are developed.
Abstract: The central objective of this paper is to develop reduced basis methods for parameter dependent transport dominated problems that are rigorously proven to exhibit rate-optimal performance when compared with the Kolmogorov $n$-widths of the solution sets. The central ingredient is the construction of computationally feasible "tight" surrogates which in turn are based on deriving a suitable well-conditioned variational formulation for the parameter dependent problem. The theoretical results are illustrated by numerical experiments for convection-diffusion and pure transport equations. In particular, the latter example sheds some light on the smoothness of the dependence of the solutions on the parameters.
94 citations
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TL;DR: G greedy basis pursuit (GBP), a new algorithm for computing sparse signal representations using overcomplete dictionaries, is introduced and experiments show that GBP can provide a fast alternative to standard linear programming approaches to basis pursuit.
Abstract: We introduce greedy basis pursuit (GBP), a new algorithm for computing sparse signal representations using overcomplete dictionaries. GBP is rooted in computational geometry and exploits equivalence between minimizing the l1-norm of the representation coefficients and determining the intersection of the signal with the convex hull of the dictionary. GBP unifies the different advantages of previous algorithms: like standard approaches to basis pursuit, GBP computes representations that have minimum l1-norm; like greedy algorithms such as matching pursuit, GBP builds up representations, sequentially selecting atoms. We describe the algorithm, demonstrate its performance, and provide code. Experiments show that GBP can provide a fast alternative to standard linear programming approaches to basis pursuit.
94 citations
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12 Dec 2005TL;DR: Three well-known optimization techniques greedy search, simulated annealing and ant colony optimization are adapted to this multi-objective context to find an optimal set of assets to invest on, as well as the optimal investments for each asset.
Abstract: The portfolio optimization problem uses mathematical approaches to model stock exchange investments. Its aim is to find an optimal set of assets to invest on, as well as the optimal investments for each asset. In the present work, the problem is treated as a multi-objective optimization problem. Three well-known optimization techniques greedy search, simulated annealing and ant colony optimization are adapted to this multi-objective context. Pareto fronts for five stock indexes are collected, showing the different behaviors of the algorithms adapted. Finally, the results are discussed.
94 citations