Mathematical and computational perspectives on the Boolean and binary rank and their relation to the real rank
Michal Parnas

TL;DR
This survey reviews the mathematical and computational aspects of Boolean and binary rank, their relation to real rank, and discusses bounds, algorithms, and complexity results, highlighting current knowledge and future research directions.
Contribution
It provides a comprehensive overview of the definitions, properties, bounds, and algorithms related to Boolean and binary rank, emphasizing their connection to real rank and computational complexity.
Findings
Summarizes key techniques for bounding binary and Boolean rank.
Discusses algorithms for computing and approximating these ranks.
Highlights the relationship between these ranks and communication complexity.
Abstract
This survey provides a comprehensive overview of the study of the binary and Boolean rank from both a mathematical and a computational perspective, with particular emphasis on their relationship to the real rank. We review the basic definitions of these rank functions and present the main alternative formulations of the binary and Boolean rank, together with their computational complexity and their deep connection to the field of communication complexity. We summarize key techniques used to establish lower and upper bounds on the binary and Boolean rank, including methods from linear algebra, combinatorics and graph theory, isolation sets, the probabilistic method, kernelization, communication protocols and the query to communication lifting technique. Furthermore, we highlight the main mathematical properties of these ranks in comparison with those of the real rank, and discuss several…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComplexity and Algorithms in Graphs · Stochastic Gradient Optimization Techniques · Sparse and Compressive Sensing Techniques
