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There is already support for BilinearOperator in `pyproximal/pyproximal/utils/bilinear.py` and the PALM optimizer; however,
they do not scale to second-order methods such as Levenberg-Marquardt (LM) …
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Basically "NuclearNorm" as currently written has some naming issues:
See doc: https://odlgroup.github.io/odl/generated/odl.solvers.functional.default_functionals.NuclearNorm.html
* First, the cu…
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We'd like to have a tensor LMO for tensor nuclear norm constraints. Therefore, an implementation of https://hal.archives-ouvertes.fr/hal-01385538/file/SeROAP_final.pdf (or some other rank 1 tensor dec…
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Hello!
Thanks for your wonderful work.
In the Optimization section of the paper, it said that "In order to optimize (2) via backpropagation, we need to compute a subgradient of the nuclear norm of a…
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why multiply by 2?
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The matrix `norm()` function could use a redesign to reflect the many matrix norms that are used in numerical analysis. The usefully computable norms can be divided into two classes:
- [ ] Hölder _p_-…
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Hi! I appreciate this work very much.
However, I notice that in the 'readme.md' file the 'SoftImpute' function is said to be inspired by the [softImpute] package for R, but there is a critical param…
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This figure seems a little too obvious to include but happy to be vetoed.
Also, while it may be true that "it is possible that the true but unknown population mean has the low-rank property but the e…
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Hi there,
Among the classical denoising technique, low-rank approximation (a.k.a PCA) is a widely known technique, and the so-called singular value threshold corresponds to the proximal operator of…
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In [1] a general low-rank inducing framework was introduced and showed many benefits compared to e.g. the nuclear norm when applied to common computer vision tasks. This was later generalized in [2].
…