CppAD | A C++ Algorithmic Differentiation Package: Home Page | Machine Learning library

by coin-or C++ Version: 20230000.0 License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | CppAD Summary

CppAD is a C++ library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. CppAD has no bugs, it has no vulnerabilities and it has low support. However CppAD has a Non-SPDX License. You can download it from GitHub.

A C++ Algorithmic Differentiation Package: Home Page

Support

Quality

Security

License

Reuse

Support

CppAD has a low active ecosystem.

It has 347 star(s) with 86 fork(s). There are 29 watchers for this library.

It had no major release in the last 12 months.

There are 5 open issues and 87 have been closed. On average issues are closed in 63 days. There are 3 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of CppAD is 20230000.0

Quality

CppAD has 0 bugs and 0 code smells.

Security

CppAD has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

CppAD code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

CppAD has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

CppAD releases are available to install and integrate.

Installation instructions are not available. Examples and code snippets are available.

It has 825 lines of code, 4 functions and 3 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

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CppAD Key Features

No Key Features are available at this moment for CppAD.

CppAD Examples and Code Snippets

No Code Snippets are available at this moment for CppAD.

Community Discussions

Trending Discussions on CppAD

Use CppADCodeGen with CMake FetchContent or ExternalProject

Function to invert Eigen matrix without branching statements for auto differentiation

QUESTION

Use CppADCodeGen with CMake FetchContent or ExternalProject

Asked 2021-Nov-25 at 10:56

I am not good with CMake, and I cannot find good explanations about how to use its FetchContent functionality. Indeed, most repositories seem to require different treatment, and the rules of such treatment defy my comprehension.

That said, here is my problem. I would like to use CppADCodeGen in my project using CMake FetchContent. Here is my code:

...

ANSWER

Answered 2021-Oct-26 at 20:48

Problems Overview

As seen in the output you've provided, there are 2 problems:

There is a target name conflict between probably CppAD and eigen. They both have the uninstall target. It can be seen here:

Source https://stackoverflow.com/questions/69686099

QUESTION

Function to invert Eigen matrix without branching statements for auto differentiation

Asked 2021-Nov-12 at 10:21

I need to invert an Eigen matrix (9x9 in my particular case) as a part of code that I want to automatically differentiate using CppAD. For this to succeed the code executing the inversion can not contain any branching like for example if or switch statements. Unfortunately, the inverse function of Eigen contains branching with makes the algorithmic differentiation of CppAD fail.

Mathematically it should be possible to come up with a formulation that does not need branching for a fixed matrix size that is guaranteed to be invertible. Is that correct?

Do you know of any library that implements such an inverse without branching?

...

ANSWER

Answered 2021-Nov-12 at 09:47

In general, it is possible to invert arbitrary large (invertible) matrices without branching, but this gets inefficient for bigger matrices. Eigen only does this for matrices up to size 4x4.

If you want the derivation of the inverse, just use the identity

Source https://stackoverflow.com/questions/69933894

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