Designing Compact ILPs via Fast Witness Verification

The standard formalization of preprocessing in parameterized complexity is given by kernelization. In this work, we depart from this paradigm and study a different type of preprocessing for problems without polynomial kernels, still aiming at producing instances that are easily solvable in practice. Specifically, we ask for which parameterized problems an instance (I,k) can be reduced in polynomial time to an integer linear program (ILP) with poly(k) constraints. We show that this property coincides with the parameterized complexity class WK[1], previously studied in the context of Turing kernelization lower bounds. In turn, the class WK[1] enjoys an elegant characterization in terms of witness verification protocols: a yes-instance should admit a witness of size poly(k) that can be verified in time poly(k). By combining known data structures with new ideas, we design such protocols for several problems, such as r-Way Cut, Vertex Multiway Cut, Steiner Tree, or Minimum Common String Partition, thus showing that they can be modeled by compact ILPs. We also present explicit ILP and MILP formulations for Weighted Vertex Cover on graphs with small (unweighted) vertex cover number. We believe that these results will provide a background for a systematic study of ILP-oriented preprocessing procedures for parameterized problems.

💡 Research Summary

The paper investigates a new form of preprocessing for parameterized problems that do not admit polynomial kernels, focusing on reductions to integer linear programs (ILPs) whose number of constraints is bounded by a polynomial in the parameter k. The authors argue that, while kernelization seeks to shrink the overall input size, many practical solvers perform better when the problem is first transformed into a compact ILP and then handed to a specialized ILP solver. To formalize this, they define a polynomial‑parameter transformation (PPT) that maps an instance (I,k) to an equivalent ILP feasibility instance (A x ≤ b, x ∈ ℤⁿ≥0) with m = poly(k) constraints. The key question is which parameterized problems admit such a PPT.

The central theoretical contribution is Theorem 1, which shows that ILP feasibility, when parameterized by m + log Δ(A) (or by m alone under the restriction Δ(A)=1 and unary b), is WK