will greatly speed up the computations. A zero entry means
        that a corresponding element in the Jacobian is identically zero.
        If provided, forces the use of 'lsmr' trust-region solver.
        If None (default) then dense differencing will be used.

    Notes
    -----
    Finite difference schemes {'2-point', '3-point', 'cs'} may be used for
    approximating either the Jacobian or the Hessian. We, however, do not allow
    its use for approximating both simultaneously. Hence whenever the Jacobian
    is estimated via finite-differences, we require the Hessian to be estimated
    using one of the quasi-Newton strategies.

    The scheme 'cs' is potentially the most accurate, but requires the function
    to correctly handles complex inputs and be analytically continuable to the
    complex plane. The scheme '3-point' is more accurate than '2-point' but
    requires twice as many operations.

    Examples
    --------
    Constrain ``x[0] < sin(x[1]) + 1.9``

    >>> from scipy.optimize import NonlinearConstraint
    >>> import numpy as np
    >>> con = lambda x: x[0] - np.sin(x[1])
    >>> nlc = NonlinearConstraint(con, -np.inf, 1.9)

    ú