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:
- funccallable f(x,*args)
- Objective function. Must return a scalar. 
- x01-D ndarray of float
- Initial guess for the independent variable(s). 
- eqconslist, optional
- A list of functions of length n such that eqcons[j](x,*args) == 0.0 in a successfully optimized problem. 
- f_eqconscallable 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. 
- ieqconslist, optional
- A list of functions of length n such that ieqcons[j](x,*args) >= 0.0 in a successfully optimized problem. 
- f_ieqconscallable 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. 
- boundslist, 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. 
- fprimecallable f(x,*args), optional
- A function that evaluates the partial derivatives of func. 
- fprime_eqconscallable 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_ieqconscallable 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) ).
- argssequence, optional
- Additional arguments passed to func and fprime. 
- iterint, optional
- The maximum number of iterations. 
- accfloat, optional
- Requested accuracy. 
- iprintint, 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 
 
- dispint, optional
- Overrides the iprint interface (preferred). 
- full_outputbool, optional
- If False, return only the minimizer of func (default). Otherwise, output final objective function and summary information. 
- epsilonfloat, optional
- The step size for finite-difference derivative estimates. 
- callbackcallable, optional
- Called after each iteration, as - callback(x), where- xis the current parameter vector.
 
- Returns:
- outndarray of float
- The final minimizer of func. 
- fxndarray of float, if full_output is true
- The final value of the objective function. 
- itsint, if full_output is true
- The number of iterations. 
- imodeint, if full_output is true
- The exit mode from the optimizer (see below). 
- smodestring, 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 reached
 - Examples - Examples are given in the tutorial.