# Check if Hessian is Postive Semi-Definite [closed]

I have a log-likelihood function, and I am trying to determine if it is convex or concave to see if I can use standard optimization techniques. So I am trying to determine if the hessian is positive (or negative) semi-definite. This is what I have done:

```
from sage.symbolic.constants import Constant
var('E,a,k')
assume(k != 0)
assume(a > 0)
assume(E,'real')
Constant('x')
f(E,a,k) = -log(a - k * (x - E))
g(E,a,k) = -(log(1-k*(x-E)/a))^2/(2*k^2*s^2)
l(E,a,k) = f + g
solve(l.hessian() >= 0, [E,a,k])
```

However, I am getting this error:

```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-90-f091cf87ab45> in <module>()
----> 1 solve(l.hessian() >= Integer(0), [E,a,k])
2
3
4
/usr/lib64/python2.7/site-packages/sage/symbolic/relation.pyc in solve(f, *args, **kwds)
816
817 if not isinstance(f, (list, tuple)):
--> 818 raise TypeError("The first argument must be a symbolic expression or a list of symbolic expressions.")
819
820 if len(f)==1:
TypeError: The first argument must be a symbolic expression or a list of symbolic expressions.
```

How can I fix this so I can get what I want? Is there a better way to do this?

Duplicate of https://ask.sagemath.org/question/434...