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=np.float64(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 `x` is 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.

`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.