[](https://auto-differentiation.github.io/python)
[](https://pypi.org/project/xad-autodiff/)
XAD is a library designed for
[automatic differentiation](https://auto-differentiation.github.io/aad/),
aimed at both beginners and advanced users. It is intended for use in
production environments, emphasizing performance and ease of use. The library
facilitates the computation of derivatives within computer programs, making
the process efficient and straightforward for a wide range of mathematical
functions, from simple arithmetic to complex calculations, ensuring accurate
and automatic derivative computations.
The Python bindings for XAD offer the following features:
- Support for both forward and adjoint modes at the first order.
- Strong exception-safety guarantees.
- High performance, as demonstrated in extensive production use.
For more details and to integrate XAD into your projects, consult the
comprehensive [documentation](https://auto-differentiation.github.io/python).
## Application Areas
Automatic differentiation has many application areas, for example:
- **Machine Learning and Deep Learning:** Training neural networks or other
machine learning models.
- **Optimization:** Solving optimization problems in engineering and finance.
- **Numerical Analysis:** Enhancing numerical solution methods for
differential equations.
- **Scientific Computing:** Simulating physical systems and processes.
- **Risk Management and Quantitative Finance:** Assessing and hedging risk in
financial models.
- **Computer Graphics:** Optimizing rendering algorithms.
- **Robotics:** Improving control and simulation of robotic systems.
- **Meteorology:** Enhancing weather prediction models.
- **Biotechnology:** Modeling biological processes and systems.
## Getting Started
Install:
```text
pip install xad-autodiff
```
Calculate first-order derivatives in adjoint mode:
```python
import xad_autodiff.adj_1st as xadj
# set independent variables
x0_ad = xadj.Real(1.0)
x1_ad = xadj.Real(1.5)
x2_ad = xadj.Real(1.3)
x3_ad = xadj.Real(1.2)
with xadj.Tape() as tape:
# and register them
tape.registerInput(x0_ad)
tape.registerInput(x1_ad)
tape.registerInput(x2_ad)
tape.registerInput(x3_ad)
# start recording derivatives
tape.newRecording()
# calculate the output
y = x0_ad + x1_ad - x2_ad * x3_ad
# register and seed adjoint of output
tape.registerOutput(y)
y.derivative = 1.0
# compute all other adjoints
tape.computeAdjoints()
# output results
print(f"y = {y}")
print(f"first order derivatives:\n")
print(f"dy/dx0 = {x0_ad.derivative}")
print(f"dy/dx1 = {x1_ad.derivative}")
print(f"dy/dx2 = {x2_ad.derivative}")
print(f"dy/dx3 = {x3_ad.derivative}")
```
For more information, see the [Documentation](https://auto-differentiation.github.io/python).
Raw data
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"description": "[](https://auto-differentiation.github.io/python)\n[](https://pypi.org/project/xad-autodiff/)\n\n\nXAD is a library designed for\n[automatic differentiation](https://auto-differentiation.github.io/aad/),\naimed at both beginners and advanced users. It is intended for use in\nproduction environments, emphasizing performance and ease of use. The library\nfacilitates the computation of derivatives within computer programs, making\nthe process efficient and straightforward for a wide range of mathematical\nfunctions, from simple arithmetic to complex calculations, ensuring accurate\nand automatic derivative computations.\n\nThe Python bindings for XAD offer the following features:\n\n- Support for both forward and adjoint modes at the first order.\n- Strong exception-safety guarantees.\n- High performance, as demonstrated in extensive production use.\n\nFor more details and to integrate XAD into your projects, consult the\ncomprehensive [documentation](https://auto-differentiation.github.io/python).\n\n## Application Areas\n\nAutomatic differentiation has many application areas, for example:\n\n- **Machine Learning and Deep Learning:** Training neural networks or other\n machine learning models.\n- **Optimization:** Solving optimization problems in engineering and finance.\n- **Numerical Analysis:** Enhancing numerical solution methods for\n differential equations.\n- **Scientific Computing:** Simulating physical systems and processes.\n- **Risk Management and Quantitative Finance:** Assessing and hedging risk in\n financial models.\n- **Computer Graphics:** Optimizing rendering algorithms.\n- **Robotics:** Improving control and simulation of robotic systems.\n- **Meteorology:** Enhancing weather prediction models.\n- **Biotechnology:** Modeling biological processes and systems.\n\n## Getting Started\n\nInstall:\n\n```text\npip install xad-autodiff\n```\n\n\nCalculate first-order derivatives in adjoint mode:\n\n```python\nimport xad_autodiff.adj_1st as xadj\n\n\n# set independent variables\nx0_ad = xadj.Real(1.0)\nx1_ad = xadj.Real(1.5)\nx2_ad = xadj.Real(1.3)\nx3_ad = xadj.Real(1.2)\n\nwith xadj.Tape() as tape:\n # and register them\n tape.registerInput(x0_ad)\n tape.registerInput(x1_ad)\n tape.registerInput(x2_ad)\n tape.registerInput(x3_ad)\n\n # start recording derivatives\n tape.newRecording()\n\n # calculate the output\n y = x0_ad + x1_ad - x2_ad * x3_ad\n\n # register and seed adjoint of output\n tape.registerOutput(y)\n y.derivative = 1.0\n\n # compute all other adjoints\n tape.computeAdjoints()\n\n # output results\n print(f\"y = {y}\")\n print(f\"first order derivatives:\\n\")\n print(f\"dy/dx0 = {x0_ad.derivative}\")\n print(f\"dy/dx1 = {x1_ad.derivative}\")\n print(f\"dy/dx2 = {x2_ad.derivative}\")\n print(f\"dy/dx3 = {x3_ad.derivative}\")\n```\n\nFor more information, see the [Documentation](https://auto-differentiation.github.io/python).\n\n",
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