Abstract
The paper presents a new development in the Multipoint Approximation Method (MAM) that makes it capable of handling large-scale problems. The approach relies on approximations built in the space of design variables within the iterative trust-region-based framework of MAM. With the purpose of solving high dimensionality problems in a reasonable time, a parallel variant of the Multipoint Approximation Method (PMAM) has been developed. It is supposed that the values of the objective function and those of the constraints are computed using distributed memory (on several cluster nodes), whereas the optimization module runs on a single node using shared memory. Numerical experiments have been carried out on a benchmark example of structural optimization.
This study was supported by the Russian Science Foundation, project No. 16-11-10150.
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Gergel, V.P., Barkalov, K.A., Kozinov, E.A., Toropov, V.V. (2018). Parallel Multipoint Approximation Method for Large-Scale Optimization Problems. In: Sokolinsky, L., Zymbler, M. (eds) Parallel Computational Technologies. PCT 2018. Communications in Computer and Information Science, vol 910. Springer, Cham. https://doi.org/10.1007/978-3-319-99673-8_13
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