[SciPy-user] Schur complement

Nils Wagner nwagner at mecha.uni-stuttgart.de
Wed May 26 09:52:20 CDT 2004


Pearu Peterson wrote:
> 
> On Wed, 26 May 2004, Nils Wagner wrote:
> 
> 
>>I am trying to solve a transcendental eigenvalue problem via the Schur 
>>complement. However, I have some trouble with optimize.fsolve.
>>
>>How can I solve this problem ?
> 
> 
> In short, use
> 
> def func(x):
>    return abs(schur(T(x[0])))
> 
> (when using optimize.fsolve then the argument x is rank-1 array and
> fsolve assumes that func is a real-valued function)
> 
> Pearu
> 
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> 
I have modified my program.

def func2(x):
  return abs(schur(T(x)))

r6 = optimize.fsolve(func2,x0)

Now I get

Traceback (most recent call last):
   File "fschur.py", line 77, in ?
     r6 = optimize.fsolve(func2,x0)
   File "/usr/lib/python2.3/site-packages/scipy/optimize/minpack.py", 
line 80, in fsolve
     check_func(func,x0,args,n,(n,))
   File 
"/usr/lib/python2.3/site-packages/scipy/optimize/common_routines.py", 
line 13, in check_func
     res = myasarray(apply(thefunc,args))
   File "fschur.py", line 63, in func2
     return abs(schur(T(x)))
   File "fschur.py", line 8, in T
     tmp[0,0] = -sin(x)
ValueError: array too large for destination
 >>>


The requirement, i.e. func is real is not mentioned in help 
(optimize.fsolve). Am I missing something ?


Nils

fsolve(func, x0, args=(), fprime=None, full_output=0, col_deriv=0, 
xtol=1.49012e
-08, maxfev=0, band=None, epsfcn=0.0, factor=100, diag=None)
     Find the roots of a function.

     Description:

       Return the roots of the (non-linear) equations defined by
       func(x)=0 given a starting estimate.

     Inputs:

       func -- A Python function or method which takes at least one
               (possibly vector) argument.
       x0 -- The starting estimate for the roots of func(x)=0.
       args -- Any extra arguments to func are placed in this tuple.
       fprime -- A function or method to compute the Jacobian of func with
               derivatives across the rows. If this is None, the
               Jacobian will be estimated.
       full_output -- non-zero to return the optional outputs.
       col_deriv -- non-zero to specify that the Jacobian function
                    computes derivatives down the columns (faster, because
                    there is no transpose operation).

     Outputs: (x, {infodict, ier, mesg})

       x -- the solution (or the result of the last iteration for an
            unsuccessful call.

       infodict -- a dictionary of optional outputs with the keys:
                   'nfev' : the number of function calls
                   'njev' : the number of jacobian calls
                   'fvec' : the function evaluated at the output
                   'fjac' : the orthogonal matrix, q, produced by the
                            QR facotrization of the final approximate
                            Jacobi matrix, stored column wise.
                   'r'    : upper triangular matrix produced by QR
                            factorization of same matrix.
                   'qtf'  : the vector (transpose(q) * fvec).
       ier -- an integer flag.  If it is equal to 1 the solution was
              found.  If it is not equal to 1, the solution was not
              found and the following message gives more information.
       mesg -- a string message giving information about the cause of
               failure.

     Extended Inputs:

      xtol -- The calculation will terminate if the relative error
              between two consecutive iterates is at most xtol.
      maxfev -- The maximum number of calls to the function. If zero,
                then 100*(N+1) is the maximum where N is the number
                of elements in x0.
      band -- If set to a two-sequence containing the number of sub-
              and superdiagonals within the band of the Jacobi matrix,




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