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@msn.com> Subject: Re: [SciPy-User] optimize.minimize - help me understand arrays as variables (Andrew Nelson) To: "scipy-user@scipy.org" <scipy-user@scipy.org>, "andyfaff@gmail.com" <andyfaff@gmail.com> Message-ID: <BLU172-W4495293CAEBC1F82EBD847D8430@phx.gbl> Content-Type: text/plain; charset="iso-8859-1"

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Date: Sun, 11 Jan 2015 17:55:32 -0700 From: KURT PETERS <peterskurt@msn.com> Subject: [SciPy-User] optimize.minimize - help me understand arrays as variables To: "scipy-user@scipy.org" <scipy-user@scipy.org> Message-ID: <BLU172-W37473350444550BD9CF90DD8430@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.

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Date: Mon, 12 Jan 2015 11:58:31 +1100 From: Andrew Nelson <andyfaff@gmail.com> Subject: Re: [SciPy-User] optimize.minimize - help me understand arrays as variables To: SciPy Users List <scipy-user@scipy.org> Message-ID: <CAAbtOZdLeL8j4=3Pc1smLd39g98ppwC8Ua44aWnk_3L4xqp7vw@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