I'm using scipy.optimize.minimize to solve/calculate 3768 variables in 314 affine transformations to transform one point cloud to another eval_fun, firstly I tried using scipy.optimize.fmin_l_bfgs_b with approx_grad to avoid implementing gradient but it kept crashing with ABNORMAL_TERMINATION_IN_LNSRCH. Then I implemented gradient func and started using minimize and it still crashes with the same error.
res = minimize(eval_func,
x0=np.array(M),
method='L-BFGS-B',
args=(scan_pts, scan_fce, scan_nrm, scan_mar, temp_pts, temp_fce, temp_nrm, temp_mar, alfa, beta, gama,edges),
options={'iprint' : 99, 'maxiter' : 100}, jac=True)
This Is complete output:
This problem is unconstrained.
Line search cannot locate an adequate point after 20 function
and gradient evaluations. Previous x, f and g restored.
Possible causes: 1 error in function or gradient evaluation;
2 rounding error dominate computation.
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 3768 M = 10
At X0 0 variables are exactly at the bounds
At iterate 0 f= 1.88129D-01 |proj g|= 9.98119D-01
ITERATION 1
---------------- CAUCHY entered-------------------
There are 0 breakpoints
GCP found in this segment
Piece 1 --f1, f2 at start point -9.3847D+02 9.3847D+02
Distance to the stationary point = 1.0000D+00
---------------- exit CAUCHY----------------------
3768 variables are free at GCP 1
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
3768 1 21 1 0 0 9.981D-01 1.881D-01
F = 0.18812870968000006
ABNORMAL_TERMINATION_IN_LNSRCH
I tried changing all values as said here: scipy.optimize.fmin_l_bfgs_b returns 'ABNORMAL_TERMINATION_IN_LNSRCH' but none of them work
What can I do?
