Consider the optimization problem \begin{align}\label{opt-lp}\tag{Primal} \begin{array}{cl} \underset{x \in \mathbb{R}^n}{\text{minimize}} & c^\top x \\ \text{subject to} & Ax = a \\ & Bx = b \end{array} \end{align} where $A \in \mathbb{R}^{m \times n}, a \in \mathbb{R}^m, B \in \mathbb{R}^{q \times n}, b \in \mathbb{R}^q$ are the problem data, and the problem has a nonempty feasible set.
I would like to introduce a partial Lagrange relaxation to only $Ax = a$ constrains, so that the partial Lagrange function is $$L(x, \lambda) =c^\top x + \lambda^\top ( Ax - a).$$ As this is a "partial" Lagrange relaxation, I define the Lagrange dual function as $$ g(\lambda) = \underset{x : Bx = b}\inf L(x, \lambda)$$ that is, I add the constraint of $Bx = b$ already. It is clear that $g(\lambda)$ lower bounds \eqref{opt-lp}.
I think the Lagrange dual problem becomes: \begin{align}\label{dual}\tag{Dual} \sup_\lambda \inf_{x : Bx =b} c^\top x + \lambda^\top (Ax - a). \end{align} My question is whether what I am doing here has a name and whether I can simply say strong duality holds between problems \eqref{opt-lp} and \eqref{dual} in a paper without going much in detail.