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Ceres docs advocate for using algorithmic differentiation instead of finite differences. Let's make one more IPy notebook example with the jacobian calculated using algopy, https://pythonhosted.org/al…
ev-br updated
9 years ago
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Currently, only first order derivatives are provided using Forward Mode Algorithmic Differentiation.
If implemented, the efficiency for first order would be much higher and second order would be poss…
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Version:
```
SCIP version 10.0.0 [precision: 8 byte] [memory: block] [mode: optimized] [LP solver: Soplex 7.1.0] [GitHash: 820c627c95]
Copyright (c) 2002-2024 Zuse Institute Berlin (ZIB)
Externa…
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Lots has changed since the docs were first written. #152 addresses a number of things, but there are a few more things that we might want to consider:
- changing all references to autodiff / automa…
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Recently, there has been a growth of interest in automatic differentiation tools used in adjoint modelling. Some popular projects in the Python and Julia worlds are [JAX](https://github.com) and
[Jul…
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**Reported by hansolsson on 17 Feb 2016 16:46 UTC**
Based on #1909 I tried to see if functions are would be complicated for automatic differentiation (or algorithmic differentiation for those that pre…
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Currently, `derivative.jl` has the two following definintions
``` .jl
second_derivative(f::Function) = second_derivative(f, derivative(f), :scalar, :central)
hessian(f::Function) = second_derivative(…
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This is a great package! Thanks for making it available.
FYI, your README should cite a few more works:
Zweig, Geoffrey and Padmanabhan, Mukund. Exact Alpha-Beta Computation in
Logarithmic Spa…
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![autodiff_roadmap_svg](https://user-images.githubusercontent.com/33411325/170226565-0fc8663c-4e31-4842-85d1-9e09abdff817.svg)
I would like to propose an initial roadmap of the Automatic differe…
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It seems that GPR library implements the core primitive used in Bayesian optimization, does it make sense to provide a high-level module for Bayesian optimization itself? With user-friendly interface …
Lupus updated
3 years ago