Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 547187
Title Decomposition-based Inner- and Outer-Refinement Algorithms for Global Optimization
Author(s) Nowak, Ivo; Breitfeld, Norman; Hendrix, E.M.T.; Njacheun-Njanzoua, Grégoire
Source Journal of Global Optimization 72 (2018)2. - ISSN 0925-5001 - p. 305 - 321.
DOI https://doi.org/10.1007/s10898-018-0633-2
Department(s) Operations Research and Logistics
WASS
Publication type Refereed Article in a scientific journal
Publication year 2018
Keyword(s) Global optimization - Decomposition method - MINLP - Successive approximation - Column generation
Abstract Traditional deterministic global optimization methods are often based on a Branch-and-Bound (BB) search tree, which may grow rapidly, preventing the method to find a good solution. Motivated by decomposition-based inner approximation (column generation) methods for solving transport scheduling problems with over 100 million variables, we present a new deterministic decomposition-based successive approximation method for general modular and/or sparse MINLPs. The new method, called Decomposition-based Inner- and Outer-Refinement, is based on a block-separable reformulation of the model into sub-models. It generates inner- and outer-approximations using column generation, which are successively refined by solving many easier MINLP and MIP subproblems in parallel (using BB), instead of searching over one (global) BB search tree. We present preliminary numerical results with Decogo (Decomposition-based Global Optimizer), a new parallel decomposition MINLP solver implemented in Python and Pyomo.
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