scipy.optimize.fmin_slsqp¶
- scipy.optimize.fmin_slsqp(func, x0, eqcons=(), f_eqcons=None, ieqcons=(), f_ieqcons=None, bounds=(), fprime=None, fprime_eqcons=None, fprime_ieqcons=None, args=(), iter=100, acc=1e-06, iprint=1, disp=None, full_output=0, epsilon=1.4901161193847656e-08, callback=None)[source]¶
Minimize a function using Sequential Least SQuares Programming
Python interface function for the SLSQP Optimization subroutine originally implemented by Dieter Kraft.
Parameters: func : callable f(x,*args)
Objective function.
x0 : 1-D ndarray of float
Initial guess for the independent variable(s).
eqcons : list, optional
A list of functions of length n such that eqcons[j](x,*args) == 0.0 in a successfully optimized problem.
f_eqcons : callable f(x,*args), optional
Returns a 1-D array in which each element must equal 0.0 in a successfully optimized problem. If f_eqcons is specified, eqcons is ignored.
ieqcons : list, optional
A list of functions of length n such that ieqcons[j](x,*args) >= 0.0 in a successfully optimized problem.
f_ieqcons : callable f(x,*args), optional
Returns a 1-D ndarray in which each element must be greater or equal to 0.0 in a successfully optimized problem. If f_ieqcons is specified, ieqcons is ignored.
bounds : list, optional
A list of tuples specifying the lower and upper bound for each independent variable [(xl0, xu0),(xl1, xu1),...] Infinite values will be interpreted as large floating values.
fprime : callable f(x,*args), optional
A function that evaluates the partial derivatives of func.
fprime_eqcons : callable f(x,*args), optional
A function of the form f(x, *args) that returns the m by n array of equality constraint normals. If not provided, the normals will be approximated. The array returned by fprime_eqcons should be sized as ( len(eqcons), len(x0) ).
fprime_ieqcons : callable f(x,*args), optional
A function of the form f(x, *args) that returns the m by n array of inequality constraint normals. If not provided, the normals will be approximated. The array returned by fprime_ieqcons should be sized as ( len(ieqcons), len(x0) ).
args : sequence, optional
Additional arguments passed to func and fprime.
iter : int, optional
The maximum number of iterations.
acc : float, optional
Requested accuracy.
iprint : int, optional
The verbosity of fmin_slsqp :
- iprint <= 0 : Silent operation
- iprint == 1 : Print summary upon completion (default)
- iprint >= 2 : Print status of each iterate and summary
disp : int, optional
Over-rides the iprint interface (preferred).
full_output : bool, optional
If False, return only the minimizer of func (default). Otherwise, output final objective function and summary information.
epsilon : float, optional
The step size for finite-difference derivative estimates.
callback : callable, optional
Called after each iteration, as callback(x), where x is the current parameter vector.
Returns: out : ndarray of float
The final minimizer of func.
fx : ndarray of float, if full_output is true
The final value of the objective function.
its : int, if full_output is true
The number of iterations.
imode : int, if full_output is true
The exit mode from the optimizer (see below).
smode : string, if full_output is true
Message describing the exit mode from the optimizer.
See also
- minimize
- Interface to minimization algorithms for multivariate functions. See the ‘SLSQP’ method in particular.
Notes
Exit modes are defined as follows
-1 : Gradient evaluation required (g & a) 0 : Optimization terminated successfully. 1 : Function evaluation required (f & c) 2 : More equality constraints than independent variables 3 : More than 3*n iterations in LSQ subproblem 4 : Inequality constraints incompatible 5 : Singular matrix E in LSQ subproblem 6 : Singular matrix C in LSQ subproblem 7 : Rank-deficient equality constraint subproblem HFTI 8 : Positive directional derivative for linesearch 9 : Iteration limit exceeded
Examples
Examples are given in the tutorial.