Blackbox and derivative-free optimization: theory, algorithms and applications

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Authors Charles Audet, Michael Kokkolaras
Journal/Conference Name Optimization and Engineering
Paper Category
Paper Abstract Blackbox optimization refers to problems where the structure of the objective and constraint functions cannot be exploited. This is often the case when their evaluation requires the execution of a (usually time-consuming) simulation using computational models that are typically inaccessible by the user. The term Derivative-Free Optimization refers to the use of algorithms that utilize only function values because their partial derivatives are either not defined or not available; gradient approximations may sometimes be obtained, but the amount of work required to ensure they are dependable may not be worth the effort. Both blackbox and derivative-free optimization have attracted significant, and still increasing, interest from researchers over the last decade. Thus, we felt that it was time to dedicate a special issue of OPTE to this topic.
Date of publication 2016
Code Programming Language Python

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