A Bad Example for Jain's Iterative Rounding Theorem for the Cover Small Cuts Problem
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Bibliographic record
Abstract
Jain's iterative rounding theorem is a well-known result in the area of approximation algorithms and, more broadly, in combinatorial optimization. The theorem asserts that LP relaxations of several problems in network design and combinatorial optimization have the following key property: for every basic solution $x$ there exists a variable $x_e$ that has value at least a constant (e.g., $x_e\geq\frac12$). We construct an example showing that this property fails to hold for the Cover Small Cuts problem. In this problem, we are given an undirected, capacitated graph $G=(V,E),u$ and a threshold value $λ$, as well as a set of links $L$ with end-nodes in $V$ and a non-negative cost for each link $\ell\in L$; the goal is to find a minimum-cost set of links such that each non-trivial cut of capacity less than $λ$ is covered by a link. This indicates that the polyhedron of feasible solutions to the LP relaxation (of Cover Small Cuts) differs in an essential way from the polyhedrons associated with several problems in combinatorial optimization. Moreover, our example shows that a direct application of Jain's iterative rounding algorithm does not give an $O(1)$ approximation algorithm for Cover Small Cuts. We mention that Bansal et al. (Algorithmica 2024) present an $O(1)$ approximation algorithm for Cover Small Cuts based on the primal-dual method of Williamson et al. (Combinatorica 1995).
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Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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