A 2-Dimensional Binary Search for Integer Pareto Frontiers

For finite integer squares, we consider the problem of learning a classification $I$ that respects Pareto domination. The setup is natural in dynamic programming settings. We show that a generalization of the binary search algorithm achieves an optimal $\theta(n)$ worst-case run time.