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scipy.differentiate.

# jacobian

scipy.differentiate.jacobian(f, x, *, tolerances=None, maxiter=10, order=8, initial_step=0.5, step_factor=2.0, step_direction=0)

  Evaluate the Jacobian of a function numerically.

  Parameters:
    fcallable

      The function whose Jacobian is desired. The signature must be:

          f(xi: ndarray) -> ndarray

      where each element of `xi` is a finite real. If the function to be differentiated accepts additional arguments, wrap it (e.g. using `functools.partial` or `lambda`) and pass the wrapped callable into `jacobian`. f must not mutate the array `xi`. See Notes regarding vectorization and the dimensionality of the input and output.

    xfloat array_like

      Points at which to evaluate the Jacobian. Must have at least one dimension. See Notes regarding the dimensionality and vectorization.

    tolerancesdictionary of floats, optional

      Absolute and relative tolerances. Valid keys of the dictionary are:

      - `atol` - absolute tolerance on the derivative

      - `rtol` - relative tolerance on the derivative

      Iteration will stop when `res.error < atol + rtol * abs(res.df)`. The default atol is the smallest normal number of the appropriate dtype, and the default rtol is the square root of the precision of the appropriate dtype.

    maxiterint, default: 10

      The maximum number of iterations of the algorithm to perform. See Notes.

    orderint, default: 8

      The (positive integer) order of the finite difference formula to be used. Odd integers will be rounded up to the next even integer.

    initial_stepfloat array_like, default: 0.5

      The (absolute) initial step size for the finite difference derivative approximation. Must be broadcastable with x and step_direction.

    step_factorfloat, default: 2.0

      The factor by which the step size is reduced in each iteration; i.e. the step size in iteration 1 is `initial_step/step_factor`. If `step_factor < 1`, subsequent steps will be greater than the initial step; this may be useful if steps smaller than some threshold are undesirable (e.g. due to subtractive cancellation error).

    step_directioninteger array_like

      An array representing the direction of the finite difference steps (e.g. for use when x lies near to the boundary of the domain of the function.) Must be broadcastable with x and initial_step. Where 0 (default), central differences are used; where negative (e.g. -1), steps are non-positive; and where positive (e.g. 1), all steps are non-negative.

  Returns:
    res_RichResult

      An object similar to an instance of `scipy.optimize.OptimizeResult` with the following attributes. The descriptions are written as though the values will be scalars; however, if f returns an array, the outputs will be arrays of the same shape.

      successbool array

        `True` where the algorithm terminated successfully (status `0`); `False` otherwise.

      statusint array

        An integer representing the exit status of the algorithm.

        - `0` : The algorithm converged to the specified tolerances.

        - `-1` : The error estimate increased, so iteration was terminated.

        - `-2` : The maximum number of iterations was reached.

        - `-3` : A non-finite value was encountered.

      dffloat array

        The Jacobian of f at x, if the algorithm terminated successfully.

      errorfloat array

        An estimate of the error: the magnitude of the difference between the current estimate of the Jacobian and the estimate in the previous iteration.

      nitint array

        The number of iterations of the algorithm that were performed.

      nfevint array

        The number of points at which f was evaluated.

      Each element of an attribute is associated with the corresponding element of df. For instance, element `i` of nfev is the number of points at which f was evaluated for the sake of computing element `i` of df.

  See also

  `derivative`, `hessian`

  Notes

  Suppose we wish to evaluate the Jacobian of a function \(f: \mathbf{R}^m \rightarrow \mathbf{R}^n\). Assign to variables `m` and `n` the positive integer values of \(m\) and \(n\), respectively, and let `...` represent an arbitrary tuple of integers. If we wish to evaluate the Jacobian at a single point, then:

  - argument x must be an array of shape `(m,)`

  - argument f must be vectorized to accept an array of shape `(m, ...)`. The first axis represents the \(m\) inputs of \(f\); the remainder are for evaluating the function at multiple points in a single call.

  - argument f must return an array of shape `(n, ...)`. The first axis represents the \(n\) outputs of \(f\); the remainder are for the result of evaluating the function at multiple points.

  - attribute `df` of the result object will be an array of shape `(n, m)`, the Jacobian.

  This function is also vectorized in the sense that the Jacobian can be evaluated at `k` points in a single call. In this case, x would be an array of shape `(m, k)`, f would accept an array of shape `(m, k, ...)` and return an array of shape `(n, k, ...)`, and the `df` attribute of the result would have shape `(n, m, k)`.

  Suppose the desired callable `f_not_vectorized` is not vectorized; it can only accept an array of shape `(m,)`. A simple solution to satisfy the required interface is to wrap `f_not_vectorized` as follows:

      def f(x):
          return np.apply_along_axis(f_not_vectorized, axis=0, arr=x)

  Alternatively, suppose the desired callable `f_vec_q` is vectorized, but only for 2-D arrays of shape `(m, q)`. To satisfy the required interface, consider:

      def f(x):
          m, batch = x.shape[0], x.shape[1:]  # x.shape is (m, ...)
          x = np.reshape(x, (m, -1))  # `-1` is short for q = prod(batch)
          res = f_vec_q(x)  # pass shape (m, q) to function
          n = res.shape[0]
          return np.reshape(res, (n,) + batch)  # return shape (n, ...)

  Then pass the wrapped callable `f` as the first argument of `jacobian`.

  References

  [1]

  Jacobian matrix and determinant, Wikipedia, https://en.wikipedia.org/wiki/Jacobian_matrix_and_determinant

  Examples

  The Rosenbrock function maps from \(\mathbf{R}^m \rightarrow \mathbf{R}\); the SciPy implementation `scipy.optimize.rosen` is vectorized to accept an array of shape `(m, p)` and return an array of shape `p`. Suppose we wish to evaluate the Jacobian (AKA the gradient because the function returns a scalar) at `[0.5, 0.5, 0.5]`.

      >>> import numpy as np
      >>> from scipy.differentiate import jacobian
      >>> from scipy.optimize import rosen, rosen_der
      >>> m = 3
      >>> x = np.full(m, 0.5)
      >>> res = jacobian(rosen, x)
      >>> ref = rosen_der(x)  # reference value of the gradient
      >>> res.df, ref
      (array([-51.,  -1.,  50.]), array([-51.,  -1.,  50.]))

  As an example of a function with multiple outputs, consider Example 4 from [1].

      >>> def f(x):
      ...     x1, x2, x3 = x
      ...     return [x1, 5*x3, 4*x2**2 - 2*x3, x3*np.sin(x1)]

  The true Jacobian is given by:

      >>> def df(x):
      ...         x1, x2, x3 = x
      ...         one = np.ones_like(x1)
      ...         return [[one, 0*one, 0*one],
      ...                 [0*one, 0*one, 5*one],
      ...                 [0*one, 8*x2, -2*one],
      ...                 [x3*np.cos(x1), 0*one, np.sin(x1)]]

  Evaluate the Jacobian at an arbitrary point.

      >>> rng = np.random.default_rng()
      >>> x = rng.random(size=3)
      >>> res = jacobian(f, x)
      >>> ref = df(x)
      >>> res.df.shape == (4, 3)
      True
      >>> np.allclose(res.df, ref)
      True

  Evaluate the Jacobian at 10 arbitrary points in a single call.

      >>> x = rng.random(size=(3, 10))
      >>> res = jacobian(f, x)
      >>> ref = df(x)
      >>> res.df.shape == (4, 3, 10)
      True
      >>> np.allclose(res.df, ref)
      True