Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/310020 
Year of Publication: 
2022
Citation: 
[Journal:] Journal of Global Optimization [ISSN:] 1573-2916 [Volume:] 86 [Issue:] 3 [Publisher:] Springer US [Place:] New York, NY [Year:] 2022 [Pages:] 573-588
Publisher: 
Springer US, New York, NY
Abstract: 
We introduce a generalization of separability for global optimization, presented in the context of a simple branch and bound method. Our results apply to continuously differentiable objective functions implemented as computer programs. A significant search space reduction can be expected to yield an acceleration of any global optimization method. We show how to utilize interval derivatives calculated by adjoint algorithmic differentiation to examine the monotonicity of the objective with respect to so called structural separators and how to verify the latter automatically.
Subjects: 
Global optimization
Algorithmic differentiation
Branch and bound
Interval adjoints
Search space reduction
Separable functions
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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