Why can't I provide NMinimize with initial points when minimizing over two variables but not one?

Feed "InitialPoints" a list of 1D vectors:

bartab = Table[{i}, {i, 1/10, 1, 1/10}];

NMinimize[(x - 2/10)^2, {x}, 
 Method -> {"Automatic", "InitialPoints" -> bartab}]
(*  {0., {x -> 0.2}}  *)

Because NMinimize needs initial "regions", not values.

According to the manual, the initial "guess" region (not value) is given as a constrain. (bold mine)

NMinimize[{x^2, 3 <= x <= 4}, {x}]

In order for NMinimize to work, it needs a rectangular initial region in which to start. This is similar to giving other numerical methods a starting point or starting points. The initial region is specified by giving each variable a finite upper and lower bound. This is done by including in the constraints, or {x,a,b} in the variables. If both are given, the bounds in the variables are used for the initial region, and the constraints are just used as constraints. If no initial region is specified for a variable x, the default initial region of is used. Different variables can have initial regions defined in different ways.

https://reference.wolfram.com/language/tutorial/ConstrainedOptimizationGlobalNumerical.html