[SciPy-User] optimize.minimize - help me understand arrays as variables (Andrew Nelson) (KURT PETERS)

[KURT PETERS]

Has ANYONE actually gotten the multivariate to work when using their own Jacobian?  
I haven't gotten any response based on my input below, but I have to believe someone has gotten it to work.
 
Regards,
Kurt
 
Re: optimize.minimize - help me understand arrays as variables (Andrew Nelson) (KURT PETERS)
> Date: Mon, 12 Jan 2015 16:26:31 -0700
> From: KURT PETERS <peterskurt at msn.com>
> Subject: Re: [SciPy-User] optimize.minimize - help me understand
> 	arrays as variables (Andrew Nelson)
> To: "scipy-user at scipy.org" <scipy-user at scipy.org>,
> 	"andyfaff at gmail.com"	<andyfaff at gmail.com>
> Message-ID: <BLU172-W4495293CAEBC1F82EBD847D8430 at phx.gbl>
> Content-Type: text/plain; charset="iso-8859-1"
> 
>  > Date: Sun, 11 Jan 2015 17:55:32 -0700
> > From: KURT PETERS <peterskurt at msn.com>
> > Subject: [SciPy-User] optimize.minimize - help me understand arrays as
> > 	variables
> > To: "scipy-user at scipy.org" <scipy-user at scipy.org>
> > Message-ID: <BLU172-W37473350444550BD9CF90DD8430 at phx.gbl>
> > Content-Type: text/plain; charset="iso-8859-1"
> > 
> >  I'm trying to use scipy.optimize.minimize.
> >   I've tried multiple "multivariate" methods that don't seem to actually take multivariate data and derivatives.  Can someone tell me how I can make the multivariate part of the solver actually work?
> >  
> > Here's an example:
> > My main function the following (typical length for N is 3):
> >  
> >   input guess is a x0=np.array([1,2,3])
> >   the optimization function returns:
> >   def calc_f3d(...):
> >       f3d = np.ones((np.max([3,N]),1)
> >       .... do some assignments to f3d[row,0] ....
> >       return np.linalg.norm(f3d)   # numpy.array that's 3x1
> >  
> > The jacobian returns a Nx3 matrix:
> > def jacob3d(...):
> >     df = np.ones((np.max([3,N]),3))
> >     ... do some assignments to df[row,col]
> >     return df               # note numpy.array that's 3x3
> >  
> > The optimize call is:
> >     OptimizeResult = optimize.minimize(
> >         fun=tdcalc.calc_f3d,
> >         x0=ract,
> >         jac=tdcalc.jacob3d,
> >         method='BFGS',
> >         args=(operdata,),
> >         tol=1.0e-8,
> >         options={'maxiter': 40000, 'xtol':1e-8})  <--- ops change based on whether using Newton-CG or BFGS
> >  
> > When I use BFGS, I get:
> > Traceback (most recent call last):
> >   File "./tdoa_calc.py", line 664, in <module>
> >     options={'maxiter': 40000, 'gtol':1e-8})
> >   File "/usr/lib64/python2.7/site-packages/scipy/optimize/_minimize.py", line 348, in minimize
> >     return _minimize_bfgs(fun, x0, args, jac, callback, **options)
> >   File "/usr/lib64/python2.7/site-packages/scipy/optimize/optimize.py", line 779, in _minimize_bfgs
> >     old_fval, old_old_fval)
> >   File "/usr/lib64/python2.7/site-packages/scipy/optimize/linesearch.py", line 95, in line_search_wolfe1
> >     c1=c1, c2=c2, amax=amax, amin=amin, xtol=xtol)
> >   File "/usr/lib64/python2.7/site-packages/scipy/optimize/linesearch.py", line 147, in scalar_search_wolfe1
> >     alpha1 = min(1.0, 1.01*2*(phi0 - old_phi0)/derphi0)
> > ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
> >  
> > When I use Newton-CG, I get:
> > Traceback (most recent call last):
> >   File "./tdoa_calc.py", line 655, in <module>
> >     options={'maxiter': 40000, 'xtol':1e-8})
> >   File "/usr/lib64/python2.7/site-packages/scipy/optimize/_minimize.py", line 351, in minimize
> >     **options)
> >   File "/usr/lib64/python2.7/site-packages/scipy/optimize/optimize.py", line 1320, in _minimize_newtoncg
> >     eta = numpy.min([0.5, numpy.sqrt(maggrad)])
> >   File "/usr/lib64/python2.7/site-packages/numpy/core/fromnumeric.py", line 1982, in amin
> >     out=out, keepdims=keepdims)
> >   File "/usr/lib64/python2.7/site-packages/numpy/core/_methods.py", line 14, in _amin
> >     out=out, keepdims=keepdims)
> > ValueError: setting an array element with a sequence.
> >             
> ========================================================================
> > Date: Mon, 12 Jan 2015 11:58:31 +1100
> > From: Andrew Nelson <andyfaff at gmail.com>
> > Subject: Re: [SciPy-User] optimize.minimize - help me understand
> > 	arrays as	variables
> > To: SciPy Users List <scipy-user at scipy.org>
> > Message-ID:
> > 	<CAAbtOZdLeL8j4=3Pc1smLd39g98ppwC8Ua44aWnk_3L4xqp7vw at mail.gmail.com>
> > Content-Type: text/plain; charset="utf-8"
> > 
> > `calc_f3d` needs to return a single number, the overall 'cost'.
> > 
> 
> the "return np.linalg.norm(f3d)" DOES return a scalar ( a single number).
>  
> numpy.linalg.norm([])  returns a single number.
>  
> Best,
> Kurt

 		 	   		  
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