Benders decomposition with adaptive oracles for large scale optimization

  • This paper proposes an algorithm to efficiently solve large optimization problems which exhibit a column bounded block-diagonal structure, where subproblems differ in right-hand side and cost coefficients. Similar problems are often tackled using cutting-plane algorithms, which allow for an iterative and decomposed solution of the problem. When solving subproblems is computationally expensive and the set of subproblems is large, cutting-plane algorithms may slow down severely. In this context we propose two novel adaptive oracles that yield inexact information, potentially much faster than solving the subproblem. The first adaptive oracle is used to generate inexact but valid cutting planes, and the second adaptive oracle gives a valid upper bound of the true optimal objective. These two oracles progressively “adapt” towards the true exact oracle if provided with an increasing number of exact solutions, stored throughout the iterations. These adaptive oracles are embedded within a Benders-type algorithm able to handle inexact information. We compare the Benders with adaptive oracles against a standard Benders algorithm on a stochastic investment planning problem. The proposed algorithm shows the capability to substantially reduce the computational effort to obtain an epsilon ϵ -optimal solution: an illustrative case is 31.9 times faster for a 1.00%convergence tolerance and 15.4 times faster for a 0.01% tolerance.

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Metadaten
Author:Nicolò Mazzi, Andreas Grothey, Ken McKinnon, Nagisa Sugishita
DOI:https://doi.org/10.1007/s12532-020-00197-0
ISSN:1867-2949
Parent Title (English):Mathematical Programming Computation
Publisher:Springer Science and Business Media LLC
Document Type:Article
Language:English
Year of Completion:2020
Volume:13
Issue:4
Page Number:21
First Page:683
Last Page:703
Mathematical Programming Computation :MPC 2021 - Issue 4
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