A data-driven inverse optimization problem (DDIOP) is the problem of estimating the objective-function parameters (weights) that explain observed optimal-solution data, and it arises in many applications, including mixed integer linear programming (MILP). In inverse optimization for MILPs, the prediction error of the features is discontinuous with respect to the weights, so applying gradient-based optimization directly is difficult. In this paper we focus on the suboptimality loss. This loss attains its minimum value, zero, if and only if the weights are exactly consistent with the observed data. We reveal a geometric structure of this loss -- it is convex and piecewise linear, and moreover the set of weights that are exactly consistent with the observed data has a positive ``thickness'' rather than being a single point or a thin boundary -- and use it to show the following. First, a broad class of gradient-based optimization methods, including projected subgradient descent, reaches exact consistency with the observed data in finitely many iterations (an exact solution is obtained in finite time). Second, for projected subgradient descent we give an explicit upper bound on the number of iterations needed to reach exact consistency. Third, when the forward problem is an integer linear program (ILP), we give this upper bound as a fully explicit iteration count determined solely by the number of samples, the dimension of the features, and the structure of the constraint coefficient matrix. Through numerical experiments, we confirm this finite-step attainment behavior.

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