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# 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)

  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 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) 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 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) 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 A list of tuples specifying the lower and upper bound for each independent variable [(xl0, xu0),(xl1, xu1),...] fprime : callable f(x,*args) A function that evaluates the partial derivatives of func. fprime_eqcons : callable f(x,*args) 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) 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 Additional arguments passed to func and fprime. iter : int The maximum number of iterations. acc : float Requested accuracy. iprint : int 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 Over-rides the iprint interface (preferred). full_output : bool If False, return only the minimizer of func (default). Otherwise, output final objective function and summary information. epsilon : float The step size for finite-difference derivative estimates.
  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.

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