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Titlebook: Large Scale Optimization; State of the Art W. W. Hager,D. W. Hearn,P. M. Pardalos Book 19941st edition Kluwer Academic Publishers 1994 algo

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書目名稱Large Scale Optimization
副標(biāo)題State of the Art
編輯W. W. Hager,D. W. Hearn,P. M. Pardalos
視頻videohttp://file.papertrans.cn/582/581360/581360.mp4
圖書封面Titlebook: Large Scale Optimization; State of the Art W. W. Hager,D. W. Hearn,P. M. Pardalos Book 19941st edition Kluwer Academic Publishers 1994 algo
描述On February 15-17, 1993, a conference on Large Scale Optimization, hosted by the Center for Applied Optimization, was held at the University of Florida. The con- ference was supported by the National Science Foundation, the U. S. Army Research Office, and the University of Florida, with endorsements from SIAM, MPS, ORSA and IMACS. Forty one invited speakers presented papers on mathematical program- ming and optimal control topics with an emphasis on algorithm development, real world applications and numerical results. Participants from Canada, Japan, Sweden, The Netherlands, Germany, Belgium, Greece, and Denmark gave the meeting an important international component. At- tendees also included representatives from IBM, American Airlines, US Air, United Parcel Serice, AT & T Bell Labs, Thinking Machines, Army High Performance Com- puting Research Center, and Argonne National Laboratory. In addition, the NSF sponsored attendance of thirteen graduate students from universities in the United States and abroad. Accurate modeling of scientific problems often leads to the formulation of large- scale optimization problems involving thousands of continuous and/or discrete vari- ables. Large s
出版日期Book 19941st edition
關(guān)鍵詞algorithms; genetic algorithms; linear optimization; Mathematica; mechanics; modeling; nonlinear optimizat
版次1
doihttps://doi.org/10.1007/978-1-4613-3632-7
isbn_softcover978-1-4613-3634-1
isbn_ebook978-1-4613-3632-7
copyrightKluwer Academic Publishers 1994
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Preliminary Computational Experience with Modified Log-Barrier Functions for Large-Scale Nonlinear s, and better conditioning are discussed. Extensive computational results are included which demonstrate that the method is clearly superior to the classic method and holds definite promise as a viable method for large-scale nonlinear programming.
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On the Complexity of a Column Generation Algorithm for Convex or Quasiconvex Feasibility Problems,s which are the previous answers of the oracle. We show that the algorithm converges in finite time and is in fact a fully polynomial approximation algorithm, provided that the feasible region has an nonempty interior. This result is based on the works of Ye [22] and Nesterov [16].
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of Florida. The con- ference was supported by the National Science Foundation, the U. S. Army Research Office, and the University of Florida, with endorsements from SIAM, MPS, ORSA and IMACS. Forty one invited speakers presented papers on mathematical program- ming and optimal control topics with a
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