Approximating the Single-Sink Link-Installation Problem in Network Design
Bibliographic record
Abstract
We initiate the algorithmic study of an important but NP-hard problem that arises commonly in network design. The input consists of the following: An undirected graph with one sink node and multiple source nodes, a specified length for each edge, and a specified demand, dem v , for each source node v. A small set of cable types, where each cable type is specified by its capacity and its cost per unit length. The cost per unit capacity per unit length of a high-capacity cable may be significantly less than that of a low-capacity cable, reflecting an economy of scale; i.e., the payoff for buying at bulk may be very high. The goal is to design a minimum-cost network that can (simultaneously) route all the demands at the sources to the sink by installing zero or more copies of each cable type on each edge of the graph. An additional restriction is that the demand of each source must follow a single path. The problem is to find a route from each source node to the sink and to assign capacity to each edge of the network such that the total costs of cables installed are minimized. We call this problem the single-sink link-installation problem. For the general problem, we introduce a new "moat-type" lower bound on the optimal value and we prove a useful structural property of near-optimal solutions: For every instance of our problem, there is a near-optimal solution whose graph is acyclic (with a cost no more than twice the optimal cost). We present efficient approximation algorithms for key special cases of the problem that arise in practice. For points in the Euclidean plane, we give an approximation algorithm with performance guarantee O(log (D/u 1 )), where D is the total demand and u 1 is the smallest cable capacity. When the metric is arbitrary, we consider the case where the network to be designed is restricted to be two level; i.e., every source-sink path has at most two edges. For this problem, we present an algorithm with performance guarantee O(log n), where n is the number of nodes in the input graph, and also show that this performance guarantee is nearly best possible.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".