Paper Reading AI Learner

Asymptotically Smaller Encodings for Graph Problems and Scheduling

2025-06-16 22:31:41
Bernardo Subercaseaux
     

Abstract

We show how several graph problems (e.g., vertex-cover, independent-set, $k$-coloring) can be encoded into CNF using only $O(|V|^2 / \lg |V|)$ many clauses, as opposed to the $\Omega(|V|^2)$ constraints used by standard encodings. This somewhat surprising result is a simple consequence of a result of Erdős, Chung, and Spencer (1983) about biclique coverings of graphs, and opens theoretical avenues to understand the success of "Bounded Variable Addition'' (Manthey, Heule, and Biere, 2012) as a preprocessing tool. Finally, we show a novel encoding for independent sets in some dense interval graphs using only $O(|V| \lg |V|)$ clauses (the direct encoding uses $\Omega(|V|^2)$), which we have successfully applied to a string-compression encoding posed by Bannai et al. (2022). As a direct byproduct, we obtain a reduction in the encoding size of a scheduling problem posed by Mayank and Modal (2020) from $O(NMT^2)$ to $O(NMT + M T^2 \lg T)$, where $N$ is the number of tasks, $T$ the total timespan, and $M$ the number of machines.

Abstract (translated)

我们展示了如何将若干图论问题(例如顶点覆盖、独立集、$k$-着色)编码为CNF(合取范式),仅使用$O(|V|^2 / \lg |V|)$数量的子句,相比之下传统的编码方法需要$\Omega(|V|^2)$的数量约束。这一令人惊讶的结果是图论中ErdÅ¡os、Chung和Spencer(1983年)关于双完全子图覆盖结果的一个简单推论,并为我们理解“有限变量添加”(Manthey, Heule 和 Biere,2012年)作为预处理工具的成功提供了一个理论途径。最后,我们提出了一种新颖的编码方法,在某些密集区间图中独立集仅使用$O(|V| \lg |V|)$数量的子句(直接编码则需要$\Omega(|V|^2)$),并且这种编码已被成功应用于Bannai等人(2022年)提出的字符串压缩问题。作为直接成果,我们还得到了Mayank和Modal(2020年)提出的一个调度问题的编码大小从$O(NMT^2)$减少到$O(NMT + M T^2 \lg T)$的结果,其中$N$表示任务数量,$T$是总时间跨度,而$M$则是机器的数量。

URL

https://arxiv.org/abs/2506.14042

PDF

https://arxiv.org/pdf/2506.14042.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model LLM Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Robot Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Time_Series Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot