Bibliographic record
Abstract
Operations Research is the application of scientific methods, especially mathematical and statistical ones, to problems of making decisions. From the huge variety of real life applications, this thesis focuses on a particular class of problems for which the placement of certain resources is in question. These tasks are referred collectively as facility location problems. This dissertation is about algorithms to solve a fundamental problem in facility location, the k-median problem. The mathematical object used here in modeling the resources and their interactions with the environment is a tree. Many other formulations are used in practice with the k-median problem, but the case of trees is special because, (i) the formulation is very simple, (ii) problems can be solved efficiently, (iii) efficient algorithms for problems in trees can be used to derive approximate solutions for general networks (Tamir (102]), and (iv) efficient algorithms for k-median problems in trees could lead to specific k-median algorithms for classes of graphs less studied, such as the graphs with bounded tree-width. Using simple techniques from computational geometry, we give the first k-median algorithm sub-quadratic in the size of the tree when k is fixed, for arbitrary trees. In the introduction, we give an overview of the main results known about the k-median problem in general. The main ideas behind our approach are also illustrated. In Chapter 2 we present a decomposition of trees that is central to our methods. In Chapter 3 we describe our approach for solving the k-median problem in trees and we give simplified algorithms for three particular cases, the 3-median problem, the k-median problem in directed trees, and the k-median problem in balanced binary trees. The following two chapters discuss two generalizations of the k-median problem, the k-median problem with positive and negative weights and the collection depots problem.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".