ANGEL is a C++ template library that enables automatic differentiation. It utilizes graph elimination techniques to minimize the cost of Jacobian matrices accumulation.
One major advantage of ANGEL is its flexibility. With this tool, you can choose from a variety of differentiation modes to suit your specific needs. Plus, you can easily integrate ANGEL with other C++ projects you're working on to add automatic differentiation capabilities seamlessly.
In terms of performance, ANGEL is incredibly efficient. The library is designed to be computationally efficient and memory-safe, so you can rely on it to handle even the toughest differentiation problems. Additionally, ANGEL is fully thread-safe, meaning you can take full advantage of multi-core processors and parallel computing capabilities.
Overall, ANGEL is a comprehensive and reliable automatic differentiation library that is definitely worth exploring. Whether you're working on scientific computing or machine learning projects, this tool can help you handle complex differentiation tasks quickly and accurately. Give it a try and see the difference it can make for your projects.
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