What is it about?
These methods avoid computing, storing and inverting the Hessian which is unfeasible for large dimensional problems.
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Why is it important?
We provide a maximum entropy derivation of a new family of BFGS-like methods. Similar results are then derived for block BFGS methods. This also yields an independent proof of a result of Fletcher 1991 and its generalisation to the block case.
Perspectives
While the effectiveness of the BFGS-like algorithms introduced in this paper needs to be tested on a significant number of large scale benchmark problems, two examples are provided where one the BFGS-like algorithms appears to perform better than standard BFGS.
Michele Pavon
Universita degli Studi di Padova
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This page is a summary of: A variational derivation of a class of BFGS-like methods, Optimization, September 2018, Taylor & Francis,
DOI: 10.1080/02331934.2018.1522635.
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