num_dual
  • Examples
  • API
num_dual
  • num_dual - generalized (hyper) dual numbers in python
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num_dual - generalized (hyper) dual numbers in python

Using dual numbers, you can compute exact derivatives of functions without writing analytical derivatives or using numeric differentiation.

Installation

You can install num_dual from PyPI using pip:

` pip install num_dual `

Build from source

To build the code from source, you need the rust compiler and maturin. You can then install the latest master directly from github:

` pip install git+https://github.com/itt-ustutt/num_dual `

  • Examples
    • First, second and third derivatives
    • Partial derivatives
    • Compute partial derivatives of multiple arguments
    • Compatibility with numpy
  • API
    • num_dual.first_derivative
      • first_derivative()
    • num_dual.gradient
      • gradient()
    • num_dual.jacobian
      • jacobian()
    • num_dual.second_derivative
      • second_derivative()
    • num_dual.hessian
      • hessian()
    • num_dual.third_derivative
      • third_derivative()
    • num_dual.second_partial_derivative
      • second_partial_derivative()
    • num_dual.partial_hessian
      • partial_hessian()
    • num_dual.third_partial_derivative
      • third_partial_derivative()
    • num_dual.third_partial_derivative_vec
      • third_partial_derivative_vec()
    • num_dual.Dual64
      • Dual64
        • Dual64.__init__()
    • num_dual.HyperDual64
      • HyperDual64
        • HyperDual64.__init__()
    • num_dual.Dual2_64
      • Dual2_64
        • Dual2_64.__init__()
    • num_dual.Dual3_64
      • Dual3_64
        • Dual3_64.__init__()
    • num_dual.HyperHyperDual64
      • HyperHyperDual64
        • HyperHyperDual64.__init__()
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© Copyright 2021, Philipp Rehner, Gernot Bauer.

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