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..
New Length Dependent Algorithm for Maximum Satisfiability Problem
Authors: Vasily Alferov, Ivan Bliznets3634-3641
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we study the computational complexity of the MAXIMUM SATISFIABILITY problem in terms of the length L of a given formula. We present an algorithm with running time O(1.0927L), hence, improving the previously known best upper bound O(1.1058L)...Our algorithms follow standard branch-and-bound technique enhanced with measure-and-conquer approach. As other algorithms with such technique our algorithms consist of reduction and branching rules. |
| Researcher Affiliation | Collaboration | Vasily Alferov,3 Ivan Bliznets 1 2 1 HSE University 2 St. Petersburg Department of Steklov Mathematical Institute of Russian Academy of Sciences 3 Jet Brains Research EMAIL, EMAIL |
| Pseudocode | Yes | R-Rule 1. Let x be a variable such that both literals x and x are contained in the same clause x x C. Then we can remove this clause, i.e. ((x x C) F , k) (F , k 1). (And other similar R-Rules and B-Rules throughout the paper presenting structured steps for the algorithm) |
| Open Source Code | No | The paper does not contain any statement or link providing concrete access to the source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper on algorithmic complexity and does not use or refer to any datasets. |
| Dataset Splits | No | This is a theoretical paper on algorithmic complexity and does not involve dataset splits for validation. |
| Hardware Specification | No | This is a theoretical paper and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not describe any experimental setup details such as hyperparameters or training configurations. |