Optimization demo looks broken

[chenliu0831]

Running ch.demo('optimization') and halted.

1.42e+02 | e1: 2.25e+01 | e2: 5.06e+01 | e3: 6.89e+01 | 
/Library/Python/2.7/site-packages/scipy/sparse/linalg/dsolve/linsolve.py:145: MatrixRankWarning: Matrix is exactly singular
  warn("Matrix is exactly singular", MatrixRankWarning)

Ctrl-c gives

/Users/chenyangliu/Desktop/BL-dev/chumpy/ch.pyc in __setattr__(self, name, value, itr)
    342             if name == 'x':
    343                 self._dirty_vars.add(name)
--> 344                 self.clear_cache(itr)
    345             #else:
    346             #    import warnings

/Users/chenyangliu/Desktop/BL-dev/chumpy/ch.pyc in clear_cache(self, itr)
    404 
    405                 for parent, parent_dict in next._parents.items():
--> 406                     object.__setattr__(parent, '_dirty_vars', parent._dirty_vars.union(parent_dict['varnames']))
    407                     parent._invalidate_cacheprop_names(parent_dict['varnames'])
    408                     todo.append(parent)

[mattloper]

What version of chumpy are you running? Is it from pipy or from latest master on github? For me, ch.demo('optimisation') is working.

[chenliu0831]

I'm using the latest master.

[mattloper]

From my testing, it seems that Mavericks exhibits no problem, Ubuntu 12.04 gives a warning but still works, and Ubuntu 14.04 hangs. I don't know what is causing the problem, or why behavior is different across platforms.

[chenliu0831]

Oh. It actually hangs on my mac. Probably the default numpy and scipy version is different across platform. I am using scipy==0.14.0, numpy==1.8.2 on my mac.

[chenliu0831]

@mattloper Sorry I accidentally deleted the previous comment. In scipy 0.14.0, scipy.sparse.linalg.spsolve is filling nan if singular matrix is detected. See diff line 139 in scipy/scipy@2344e41

[mattloper]

Finally fixed this, by falling back to scipy.sparse.linalg.lsqr when warnings are fired (typically with a singular matrix). The problem is that when any single column of the Jacobian is all-zeros, the predicted hessian is non-invertable. Sometimes such zero-columns indicates user-error (optimizing over vars that don't affect output), other times not.