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Home > Computing and Information Technology > Business applications > Enterprise software > Convergences of Stochastic Optimization Algorithms: (English)
Convergences of Stochastic Optimization Algorithms: (English)

Convergences of Stochastic Optimization Algorithms: (English)

          
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About the Book

This dissertation, "Convergences of Stochastic Optimization Algorithms" by 李國誠, Kwok-shing, Lee, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled 'Convergences of Stochastic Optimization Algorithms' submitted by Lee Kwok Shing for the degree of Master of Philosophy at the University of Hong Kong in August, 1997 A stochastic algorithm is one of the approaches to solve the optimization problem. With the inspiration from the nature, researchers invent different stochastic algorithms for optimization. These include the Genetic Algorithm whichanalogiesto the genetics fromthe biology, the EvolutionStrategieswhich borrows the concept of evolution as well as the Simulated Annealing which simulates the annealing process from solid state physics. Eventhoughthese algorithmsoriginatefromdifferentarea, they sharemany similarities. In this thesis, a general model of the stochastic algorithm for opti- mization is proposed. Usually, the stochastic optimization algorithm composes the candidate generation a well as the selection procedures. However, unlike the deterministic algorithm, the randomness is added to the procedures. Math- ematical model of the general stochastic optimization algorithm is given based on the probability distribution of the candidate in the population. Convergenceis animportantpropertyofthe stochastic optimizationprocess which guarantees the algorithm is able to find the optimum. However, not all the instances of the general stochastic algorithm converge. Therefore to ensure the convergence of an stochastic algorithm, certain conditions are added to the general framework. Moreover, in practice, the optimum is not know a priori so that the convergence measure is required. A very simple stochastic algorithm for optimization is used to illustrate the uses of the general framework. Unlike other well-known stochastic algorithms, this simple algorithm requires neither the sophisticated candidate generation procedure nor complicated parameters changing schedule. However, this algo- rithm converges though its performance is not guaranteed. DOI: 10.5353/th_b3025632 Subjects: Stochastic processesMathmatical optimizationAlgorithms


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Product Details
  • ISBN-13: 9781374720510
  • Publisher: Open Dissertation Press
  • Publisher Imprint: Open Dissertation Press
  • Height: 279 mm
  • No of Pages: 100
  • Spine Width: 8 mm
  • Width: 216 mm
  • ISBN-10: 1374720518
  • Publisher Date: 27 Jan 2017
  • Binding: Hardback
  • Language: English
  • Series Title: English
  • Weight: 531 gr


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