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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
Threshold Minimum Cut with Terminal Quotas: Logarithmic a...
Qi Duan · 2026-06-13 · via cs.DS updates on arXiv.org

We study threshold minimum cut problems with a distinguished root vertex, a set of terminals, and a quota. In the threshold minimum edge cut problem (\TMEC), the goal is to find a minimum-cost edge cut that disconnects at least $k$ terminals from the root. In the threshold minimum node cut problem (\TMNC), the goal is to delete a minimum-cost set of nonterminal, nonroot vertices so that at least $k$ terminals become disconnected from the root. We prove three approximation guarantees. First, undirected general-graph \TMEC{} admits a randomized polynomial-time expected $O(\log n)$ approximation via a Räcke-style cut-dominating tree decomposition and an exact dynamic program on trees. A standard repetition argument gives the same asymptotic ratio with high probability. Second, planar \TMEC{} admits a factor-$2$ approximation by reducing the threshold condition to planar weighted balanced cut. Third, bounded-degree planar \TMNC{} admits a $2Δ$-approximation, where $Δ$ is the maximum degree of a deletable vertex, by reducing the node-cost problem to the planar edge-cut problem on the same graph. The results separate exact-quota guarantees from bicriteria small-set-expansion-type guarantees and identify the unbounded-degree planar node-cut case as the main remaining obstacle.