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The Algorithmics of Write Optimization
2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2018Write-optimized dictionaries (WODs), such as LSM trees and B^epsilon trees, are increasingly used in databases and file systems. Such data structures support very fast insertions without sacrificing lookup performance. This talk explains how WODs can substantially reduce the I/O cost of many workloads, enabling some applications to scale by orders of ...
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Optimization of algorithms with OPAL
Mathematical Programming Computation, 2014zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Charles Audet +2 more
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Fireworks Algorithm for Optimization
2010Inspired by observing fireworks explosion, a novel swarm intelligence algorithm, called Fireworks Algorithm (FA), is proposed for global optimization of complex functions In the proposed FA, two types of explosion (search) processes are employed, and the mechanisms for keeping diversity of sparks are also well designed In order to demonstrate the ...
Ying Tan 0002, Yuanchun Zhu
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A hybrid grasshopper optimization algorithm with bat algorithm for global optimization
Multimedia Tools and Applications, 2020This paper introduces a hybrid grasshopper optimization algorithm with bat algorithm (BGOA) for global optimization. In the BGOA, the Levy flight with variable coefficient is employed to enhance the exploration capability of the GOA. Then, the local search operation of bat algorithm (BA) is combined to balance the exploration and exploitation ...
Shenghan Yue, Hongbo Zhang
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Seasons optimization algorithm
Engineering with Computers, 2020This paper introduces a new stochastic bio-inspired optimization algorithm, denoted as seasons optimization (SO) algorithm. This algorithm is inspired by the growth cycle of trees in different seasons of a year. It is an iterative and population-based algorithm working with a population of initial solutions known as a forest.
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2004
Assume we are trying to learn a concept class C of VC dimension d with respect to an arbitrary distribution. There is PAC sample size bound that holds for any algorithm that always predicts with some consistent concept in the class C (BEHW89): \(O(\frac{1}{\epsilon}(dlog\frac{1}{\epsilon}+log\frac{1}{\epsilon}))\), where e and δ are the accuracy and ...
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Assume we are trying to learn a concept class C of VC dimension d with respect to an arbitrary distribution. There is PAC sample size bound that holds for any algorithm that always predicts with some consistent concept in the class C (BEHW89): \(O(\frac{1}{\epsilon}(dlog\frac{1}{\epsilon}+log\frac{1}{\epsilon}))\), where e and δ are the accuracy and ...
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Optimality of pocket algorithm
1996Many constructive methods use the pocket algorithm as a basic component in the training of multilayer perceptrons. This is mainly due to the good properties of the pocket algorithm confirmed by a proper convergence theorem which asserts its optimality. Unfortunately the original proof holds vacuously and does not ensure the asymptotical achievement of ...
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On the Optimality of Feautrier’s Scheduling Algorithm
Concurrency and Computation: Practice and Experience, 2002AbstractFeautrier's scheduling algorithm is the most powerful existing algorithm for parallelism detection and extraction, but it has always been known to be suboptimal. However, the question as to whether it may miss some parallelism because of its design has not been answered. We show that this is not the case.
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1995
Most learning algorithms for single neuron are not able to provide for any classification problem the weight vector which satisfies the maximum number of input-output relations contained in the training set. An important exception is given by the pocket algorithm: it repeatedly executes the perceptron algorithm and maintains (in the pocket) the weight ...
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Most learning algorithms for single neuron are not able to provide for any classification problem the weight vector which satisfies the maximum number of input-output relations contained in the training set. An important exception is given by the pocket algorithm: it repeatedly executes the perceptron algorithm and maintains (in the pocket) the weight ...
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On the optimality of inclusion algorithms
1986In this paper a general concept of inclusion algorithm is introduced. Any inclusion algorithm provides a set that includes the solution of a given problem. Inclusion algorithms are studied with respect to the information used by them.
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