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PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Grouped Color Deletion, Lasserre Exactness and Clique-Sum...
Georgios Stamoulis · 2026-04-28 · via cs.DS updates on arXiv.org

We study the rainbow matching (RM) problem: given an edge-colored graph, find a maximum matching with at most one edge of each color. Rainbow matchings correspond to stable sets in the \emph{augmented} graph $H$ obtained from the line graph by completing each color class into a clique. For a hereditary graph class $\mathcal{X}$, we introduce the parameter $κ_{\mathcal{X}}$ to be the minimum number of colors whose deletion places the \emph{residual} augmented graph in $\mathcal{X}$. We show that this parameter has two complementary flavors. From a polyhedral side, if $\mathcal{X}$ is uniformly rank-$r$ exact, then deleting $k$ colors to obtain a residual augmented graph in $\mathcal{X}$ implies exactness of the Lasserre hierarchy at level $k+r$. This yields, in particular, exactness at level $k+1$ for deletion to perfect, and exactness at level $k+r$ for deletion to $h$-perfect residual graphs of bounded odd-hole rank $r$. Our second result is structural. We show that the right object in this case is the \emph{color-intersection} graph $Γ$ that impacts the topology of the conflict graph $H$ as follows: articulation colors in $Γ$ induce clique-sum decompositions in $H$, so residual obstructions for clique-sum-local hereditary classes $\mathcal{X}$ are embedded in individual blocks. Thus we can test membership of the residual graph in these target classes in a blockwise manner. As a consequence, we give an exact dynamic programming algorithm for computing the deletion parameter when $Γ$ has blocks of bounded size. Finally, once such a deletion set is given, RM can be solved by branching over the deleted color classes and solving residual instances. We also show that computing this parameter is \textbf{NP}-hard already in the chordal targets but it is FPT for classes $\mathcal{X}$ characterized by a set of forbidden induced subgraphs of bounded size.