An interior-point trust-region algorithm for quadratic stochastic symmetric programming

© 2017 by the Mathematical Association of Thailand. All rights reserved. Stochastic programming is a framework for modeling optimization problems that involve uncertainty. In this paper, we study two-stage stochastic quadratic symmetric programming to handle uncertainty in data defining (Deter-minis...

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Main Authors: Phannipa Kabcome, Thanasak Mouktonglang
Format: Journal
Published: 2018
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http://cmuir.cmu.ac.th/jspui/handle/6653943832/57523
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-575232018-09-05T03:45:07Z An interior-point trust-region algorithm for quadratic stochastic symmetric programming Phannipa Kabcome Thanasak Mouktonglang Mathematics © 2017 by the Mathematical Association of Thailand. All rights reserved. Stochastic programming is a framework for modeling optimization problems that involve uncertainty. In this paper, we study two-stage stochastic quadratic symmetric programming to handle uncertainty in data defining (Deter-ministic) symmetric programs in which a quadratic function is minimized over the intersection of an affine set and a symmetric cone with finite event space. Twostage stochastic programs can be modeled as large deterministic programming and we present an interior point trust region algorithm to solve this problem. Numerical results on randomly generated data are available for stochastic symmetric programs. The complexity of our algorithm is proved. 2018-09-05T03:45:07Z 2018-09-05T03:45:07Z 2017-01-01 Journal 16860209 2-s2.0-85018941173 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85018941173&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/57523
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Mathematics
spellingShingle Mathematics
Phannipa Kabcome
Thanasak Mouktonglang
An interior-point trust-region algorithm for quadratic stochastic symmetric programming
description © 2017 by the Mathematical Association of Thailand. All rights reserved. Stochastic programming is a framework for modeling optimization problems that involve uncertainty. In this paper, we study two-stage stochastic quadratic symmetric programming to handle uncertainty in data defining (Deter-ministic) symmetric programs in which a quadratic function is minimized over the intersection of an affine set and a symmetric cone with finite event space. Twostage stochastic programs can be modeled as large deterministic programming and we present an interior point trust region algorithm to solve this problem. Numerical results on randomly generated data are available for stochastic symmetric programs. The complexity of our algorithm is proved.
format Journal
author Phannipa Kabcome
Thanasak Mouktonglang
author_facet Phannipa Kabcome
Thanasak Mouktonglang
author_sort Phannipa Kabcome
title An interior-point trust-region algorithm for quadratic stochastic symmetric programming
title_short An interior-point trust-region algorithm for quadratic stochastic symmetric programming
title_full An interior-point trust-region algorithm for quadratic stochastic symmetric programming
title_fullStr An interior-point trust-region algorithm for quadratic stochastic symmetric programming
title_full_unstemmed An interior-point trust-region algorithm for quadratic stochastic symmetric programming
title_sort interior-point trust-region algorithm for quadratic stochastic symmetric programming
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85018941173&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/57523
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