86f4e384b066cecf50dec65c8dfa48aaddfbb17d,scipy/optimize/_minimize.py,,minimize,#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,42

Before Change

    // check gradient vector
    if meth == "trust-constr":
        if type(jac) is bool:
            if jac:
                fun = MemoizeJac(fun)
                jac = fun.derivative
            else:
                jac = "2-point"
        elif jac is None:
            jac = "2-point"
        elif not callable(jac) and jac not in ("2-point", "3-point", "cs"):
            raise ValueError("Unsupported jac definition.")
    else:
        if jac in ("2-point", "3-point", "cs"):
            if jac in ("3-point", "cs"):
                warn("Only "trust-constr" method accept %s "

After Change

             RuntimeWarning)

    // check gradient vector
    if callable(jac):
        pass
    elif jac is True:
        // fun returns func and grad
        fun = MemoizeJac(fun)
        jac = fun.derivative
    elif (jac in FD_METHODS and
          meth in ["trust-constr", "bfgs", "cg", "l-bfgs-b", "tnc"]):
        // finite differences
        pass
    elif meth in ["trust-constr"]:
        // default jac calculation for this method
        jac = "2-point"
    elif jac is None or bool(jac) is False:
        // this will cause e.g. LBFGS to use forward difference, absolute step
        jac = None
    else:
        // default if jac option is not understood
        jac = None

    // set default tolerances
    if tol is not None:
        options = dict(options)
        if meth == "nelder-mead":
            options.setdefault("xatol", tol)

Instances