Optimal Extended Formulations from Optimal Dynamic Programming Algorithms

Authors: Mateus de Oliveira Oliveira, Wim Van den Broeck

IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical In this work, we establish a sharp connection between both approaches by showing that if a vertex-subset problem Π admits a solution-preserving dynamic programming algorithm that produces tables of size at most α(k, n) when processing a tree decomposition of width at most k of an n-vertex graph G, then the polytope PΠ(G) defined as the convex-hull of solutions of Π in G has extension complexity at most O(α(k, n) n). Additionally, this upper bound is optimal under the exponential time hypothesis (ETH).
Researcher Affiliation Academia Department of Computer and Systems Sciences, Stockholm University, Sweden Department of Informatics, University of Bergen, Norway
Pseudocode No The paper describes the components of a DP-core and provides an example for Independent Set by specifying actions for Leaf, Intro Vertex, etc. This is a description of an algorithm's logic, but not a formally structured pseudocode or algorithm block.
Open Source Code No The paper does not provide concrete access to source code. There are no links to repositories or explicit statements about code availability.
Open Datasets No The paper is theoretical and does not conduct experiments involving datasets. It discusses combinatorial problems like INDEPENDENT SET as mathematical examples, not as empirical datasets for training.
Dataset Splits No The paper is theoretical and does not describe experiments with dataset splits. Therefore, no specific dataset split information for validation is provided.
Hardware Specification No The paper is theoretical and does not describe experiments that would require specific hardware. No hardware specifications are mentioned.
Software Dependencies No The paper is theoretical and does not describe experiments that would require specific software dependencies. No specific ancillary software details with version numbers are provided.
Experiment Setup No The paper is theoretical and does not describe experiments. Therefore, no specific experimental setup details (like hyperparameters or training configurations) are provided.