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..
Simple Randomized Rounding for Max-Min Eigenvalue Augmentation
Authors: Jourdain Lamperski, Haeseong Yang, Oleg Prokopyev
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that a simple randomized rounding method provides a constant-factor approximation if the optimal increase is sufficiently large, specifically, if OPT β Ξ»min(M) = β¦(R ln k), where OPT is the optimal value, and R is the maximum trace of an augmentation matrix. To establish the guarantee, we derive a matrix concentration inequality that is of independent interest. ... Finally, we plan to explore the empirical approximation performance of simple randomized rounding through a computational study. |
| Researcher Affiliation | Academia | 1Department of Industrial Engineering, University of Pittsburgh, 1025 Benedum Hall, Pittsburgh, PA 15261, USA 2Department of Business Administration, University of Zurich, Plattenstrasse 14, CH 8032 Zurich. Correspondence to: Jourdain Lamperski <EMAIL>. |
| Pseudocode | No | The paper describes a 'simple randomized rounding method' and its theoretical analysis, but it does not present this method or any other procedure in a structured pseudocode or algorithm block. |
| Open Source Code | No | Finally, we plan to explore the empirical approximation performance of simple randomized rounding through a computational study. |
| Open Datasets | No | The paper discusses theoretical problems like 'Bayesian E-optimal design' and 'maximum algebraic connectivity augmentation problems' as applications, which involve general problem formulations (e.g., 'design points x1, . . . , xm Rn'). It does not, however, conduct experiments using specific, named datasets nor provide access information for any such datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments that would involve dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical derivations and proofs; therefore, no hardware specifications for experiments are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper presents theoretical results, including a matrix concentration inequality and an approximation guarantee for a randomized rounding method. There are no experimental results, and thus no details on experimental setup or hyperparameters are provided. |