Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Earliest-Completion Scheduling of Contract Algorithms with End Guarantees
Authors: Spyros Angelopoulos, Shendan Jin
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition, we present computational results on its implementation which demonstrate that it achieves a considerable improvement over the known schedule that optimizes the acceleration ratio, but is oblivious of L. The paper is structured as follows: ... Section 5 provides a computational evaluation of the schedule. |
| Researcher Affiliation | Academia | Spyros Angelopoulos and Shendan Jin Sorbonne Universit e, CNRS, Laboratoire d Informatique de Paris 6, LIP6, F-75252 Paris, France EMAIL |
| Pseudocode | Yes | Algorithm 1 summarizes the steps needed to obtain the optimal schedule. Algorithm 1: Earliest-completion scheduling of contract algorithms with end guarantee L |
| Open Source Code | No | The paper does not provide any explicit statements about the release of source code or links to a code repository. |
| Open Datasets | No | The paper focuses on theoretical algorithm design and computational evaluation of a schedule based on mathematical parameters (L, n) rather than traditional datasets. It does not mention using any publicly available or open datasets for training purposes. |
| Dataset Splits | No | The paper does not use traditional datasets or machine learning models, and therefore does not discuss training, validation, or test splits. |
| Hardware Specification | No | The paper mentions a "computational evaluation" and "implementation" but does not specify any hardware details (e.g., CPU, GPU models, memory, or specific computing environments) used for the experiments. |
| Software Dependencies | No | The paper discusses linear programming (LP) as a technique but does not specify any particular software, libraries, or solvers with version numbers that were used for the implementation or computational evaluation. |
| Experiment Setup | Yes | In this section we present computational results on the implementation of our schedule, whose completion time recall we denote by T (L). ... We choose τ to be equal to 1, and L to be integral in the range [1, 10^6]. Figure 1 illustrates the completion times of the two schedules for n = 5. ... Figure 2 illustrates the ratio Texp(L)/T (L) for n {1, 2, 20}. |