Modeling and Optimization: Theory and Applications : MOPTA, Bethlehem, PA, USA, August 2016 Selected Contributions (Springer Proceedings in Mathematics & Statistics)

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Modeling and Optimization: Theory and Applications : MOPTA, Bethlehem, PA, USA, August 2016 Selected Contributions (Springer Proceedings in Mathematics & Statistics)

Takáč, Martin (EDT)/ Terlaky, Tamás (EDT)

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Springer International Publishing AG（2017/11発売）
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製本 Hardcover:ハードカバー版／ページ数 162 p.
商品コード 9783319666150

Full Description

This volume contains a selection of contributions that were presented at the Modeling and Optimization: Theory and Applications Conference (MOPTA) held at Lehigh University in Bethlehem, Pennsylvania, USA on August 17-19, 2016. The conference brought together a diverse group of researchers and practitioners, working on both theoretical and practical aspects of continuous or discrete optimization. Topics presented included algorithms for solving convex, network, mixed-integer, nonlinear, and global optimization problems, and addressed the application of deterministic and stochastic optimization techniques in energy, finance, logistics, analytics, health, and other important fields. The contributions contained in this volume represent a sample of these topics and applications and illustrate the broad diversity of ideas discussed at the meeting.

Stochastic Decision Problems with Multiple Risk-averse Agents (Getachew K. Befekadu, Alexander Veremyev, Vladimir Boginski, Eduardo L. Pasiliao).- Optimal Packing of General Ellipses in a Circle (Frank J. Kampas, János D. Pintér, Ignacio Castillo).- Column Generation Approach to the Convex Recoloring Problem on a Tree (Sunil Chopra, Ergin Erdem, Eunseok Kim, Sangho Shim).- A Variational Inequality Formulation of a Migration Model with Random Data (Baasansuren Jadamba, Fabio Raciti).- Identification in Mixed Variational Problems by Adjoint Methods with Applications (M. Cho, B. Jadamba, A. A. Khan, A. A. Oberai, M. Sama).- Minimization of the Lp-norm, p ≥ 1 of Dirichlet-type Boundary Controls for the 1D Wave Equation (Ilya Smirnov, Anastasia Dmitrieva).- Projected Semi-Stochastic Gradient Descent Method with Mini-Batch Scheme under Weak Strong Convexity Assumption (JieLiu and Martin Takáč).- Exact Separation of k-Projection Polytope Constraints (Elspeth Adams, Miguel F. Anjos).- Univariate Polynomial Optimization with Sum-of-Squares Interpolants (Dávid Papp).