Geometrically, scaling and preconditioning seem to address similar challenges in optimization. However, these two concepts are implemented very differently. Take trust region Newton method, as an example. When a problem is poorly scaled, an elliptical trust region is recommended. Is it possible to formulate an equivalent preconditioner based approach such that one works with spherical trust regions?

update: Section 7.5 in Practical Optimization by Gill , Murray & Wright gives a clear connection between variable scaling and preconditioning the hessian.


The ideas are certainly related, at the very least if your preconditioner corresponds to a symmetric and positive definite matrix. This is because in that case, preconditioning simply means using a different inner product, i.e., a different metric. This can be interpreted as saying that what is a very elongated ellipsoid in the usual $l_2$ metric may turn out to be much closer to a sphere in the preconditioner metric. This picture is valid because the application of an SPD preconditioner can be interpreted as a rotation, axis-parallel scaling, and inverse rotation.

The picture becomes much less clear to interpretation if you use preconditioners that correspond to indefinite or non-symmetric matrices since these can then no longer be interpreted as a simple change in metric.

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    $\begingroup$ Err, if the preconditioner varies from point to point I'm not sure it's so simple. Then you have a spatially varying inner product (ie., you are optimizing over a non-flat Riemannian manifold diffeomorphic to $\mathbb{R}^n$). Is there an easy transformation of the problem such that a black-box spherical trust region solver would generate the exact same sequence of iterates as a trust region solver with a spatially varying preconditioner? I've been thinking about this and haven't been able to figure it out. $\endgroup$ – Nick Alger Jun 7 '15 at 18:16
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    $\begingroup$ I don't know, but you should look for the term "variable metric" optimization methods. I think that there are papers by Deuflhard from the 1980s and 1990s worth reading. $\endgroup$ – Wolfgang Bangerth Jun 7 '15 at 21:55

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