Many bilevel and robust optimization problems are complete for the second level of the polynomial hierarchy, but such results are usually proven one problem at a time with problem-specific reductions. A recurring obstacle is that the lower level must select an optimal solution rather than merely a feasible one, whereas standard reductions preserve only whether a solution exists, not which solutions are optimal. We introduce linear objective problems (LOP), a framework for optimization problems whose solutions are explicit subsets of a ground set and whose objective is linear in these elements. We define tight solution-embedding reductions, which map the optimal solutions of one problem element by element onto the optimal solutions of another, without having to recognize optimal solutions, which is coNP-hard in general. This gives a notion of LOP-completeness, with Satisfiability as the canonical complete problem, even with unit weights. We show that many classical problems are LOP-complete, including Vertex Cover, Dominating Set, TSP, Knapsack, and Steiner Tree, and we introduce a duality that transfers LOP-completeness between minimization and maximization problems. As our main application, we prove a meta-theorem for min-max regret optimization under interval uncertainty: for every LOP-complete problem, the min-max regret version is $Σ_2^p$-complete. Instead of a separate proof for each nominal problem, it suffices to show that the nominal problem is LOP-complete. This recovers known results for the interval min-max regret versions of Knapsack and Maximum Benefit Set Cover and yields completeness for over 50 further problems.

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