Algorithmic discrepancy beyond partial coloring

The partial coloring method is one of the most powerful and widely used method in combinatorial discrepancy problems. However, in many cases it leads to sub-optimal bounds as the partial coloring step must be iterated a logarithmic number of times, and the errors can add up in an adversarial way. We give a new and general algorithmic framework that overcomes the limitations of the partial coloring method and can be applied in a black-box manner to various problems. Using this framework, we give new improved bounds and algorithms for several classic problems in discrepancy. In particular, for Tusnady's problem, we give an improved O(log² n) bound for discrepancy of axis-parallel rectangles and more generally an O_d(log^d n) bound for d-dimensional boxes in ℝ^d. Previously, even non-constructively, the best bounds were O(log^2.5 n) and O_d(log^d+0.5 n) respectively. Similarly, for the Steinitz problem we give the first algorithm that matches the best known non-constructive bounds due to Ba-naszczyk in the l_∞ case, and improves the previous algorithmic bounds substantially in the l₂ case. Our framework is based upon a substantial generalization of the techniques developed recently in the context of the Komlós discrepancy problem.