This constraint

```
t[0] + t[1] = 1
```

would be an equality (`type="eq"`

) constraint, where you make a function that must equal zero:

```
def con(t):
return t[0] + t[1] - 1
```

Then you make a `dict`

of your constraint (list of dicts if more than one):

```
cons = {'type':'eq', 'fun': con}
```

I’ve never tried it, but I believe that to keep `t`

real, you could use:

```
con_real(t):
return np.sum(np.iscomplex(t))
```

And make your `cons`

include both constraints:

```
cons = [{'type':'eq', 'fun': con},
{'type':'eq', 'fun': con_real}]
```

Then you feed `cons`

into `minimize`

as:

```
scipy.optimize.minimize(func, x0, constraints=cons)
```