Class FminSLSQP
source code
object --+
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FminWrapper --+
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FminSLSQP
Abstract class to generate wrappers around scipy.optimize fmin_*
functions.
Parameters
-----------
criterion : Criterion
A criterion function with __call__ and gradient methods.
x0 : ndarray (None)
First guess
args=() : tuple
Extra arguments for the criterion function
kwargs : dict
Parameters of the fmin_function
fmin function docstring
------------------------
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](x0,*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](x0,*args) >= 0.0 in a successfully optimized
problem.
f_ieqcons : callable f(x0,*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
-------
x : 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.
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 :ref:`in the tutorial <tutorial-sqlsp>`.
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Inherited from FminWrapper :
__init__ ,
first_guess
Inherited from object :
__delattr__ ,
__format__ ,
__getattribute__ ,
__hash__ ,
__new__ ,
__reduce__ ,
__reduce_ex__ ,
__repr__ ,
__setattr__ ,
__sizeof__ ,
__str__ ,
__subclasshook__
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__doc__ = FminWrapper.__doc__+ opt.fmin_slsqp.__doc__
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Inherited from object :
__class__
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