Most descriptions of the Dantzig-Wolfe decomposition, I have seen end up with subproblems like this:
$$\min_{x_j \in \mathbb{R}^n} \{ (\pi A_j - c_j)x_j \mid x_j \in P_j \}$$
They argue that $P_j$ can be described with a finite number of extreme points and extreme rays, so the process of repeatedly generating columns as optimal solutions of the subproblems will terminate eventually. However, most of these descriptions make the assumption that the variables $x_j$ are bounded (non-negative) or that $P_j$ is bounded. I understand that a free variable can be replaced by the difference of two non-negative ones and this seems to be a standard way to satisfy this assumption; for example in [Teb01]. In my case it would be simpler to think about free variables, though.
My question is about the case where all variables $x_j$ are free, so we did not do the transformation to bounded variables. Is the algorithm of repeatedly adding a column to the master problem still finite, if the added column is either (1) an extreme point of $P_j$, or (2) a ray (but not necessarily an extreme ray) of $P_j$ where $(\pi A_j - c_j)x_j < 0$?
I looked in the original paper [DW60] where Theorem 3 seems to say as much but I don't understand it sufficiently well to be sure that there are no addition assumptions about $P_j$.
References
[Teb01] James R. Tebboth, A Computational Study of Dantzig-Wolfe Decomposition, Ph.D. thesis, University of Buckingham, 2001.
[DW60] George B. Dantzig and Philip Wolfe. Decomposition Principle for Linear Programs, Operations Research 8 (1960), no. 1, 101-111.