minimize(method=’SLSQP’)

scipy.optimize.minimize(fun, x0, args=(), method='SLSQP', jac=None, bounds=None, constraints=(), tol=None, callback=None, options={'func': None, 'maxiter': 100, 'ftol': 1e-06, 'iprint': 1, 'disp': False, 'eps': 1.4901161193847656e-08, 'finite_diff_rel_step': None})

Minimize a scalar function of one or more variables using Sequential Least Squares Programming (SLSQP).

See also

For documentation for the rest of the parameters, see scipy.optimize.minimize

Options

——-

ftolfloat

Precision goal for the value of f in the stopping criterion.

epsfloat

Step size used for numerical approximation of the Jacobian.

dispbool

Set to True to print convergence messages. If False, verbosity is ignored and set to 0.

maxiterint

Maximum number of iterations.

finite_diff_rel_stepNone or array_like, optional

If jac in [‘2-point’, ‘3-point’, ‘cs’] the relative step size to use for numerical approximation of jac. The absolute step size is computed as h = rel_step * sign(x) * max(1, abs(x)), possibly adjusted to fit into the bounds. For method='3-point' the sign of h is ignored. If None (default) then step is selected automatically.