Exhibiting a deep connection between purely geometric problems and real algebra, the complexity class plays a crucial role in the study of geometric problems. Sometimes is referred to as the 'real analog' of NP. While NP is a class of computational problems that deals with existentially quantified variables, deals with existentially quantified variables. In analogy to and in the famous polynomial hierarchy, we study the complexity classes and with variables. Our main interest is the Area Universality problem, where we are given a plane graph , and ask if for each assignment of areas to the inner faces of , there exists a straight-line drawing of realizing the assigned areas. We conjecture that Area Universality is -complete and support this conjecture by proving - and -completeness of two variants of Area Universality. To this end, we introduce tools to prove -hardness and membership. Finally, we present geometric problems as candidates for -complete problems. These problems have connections to the concepts of imprecision, robustness, and extendability.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10244296PMC
http://dx.doi.org/10.1007/s00454-022-00381-0DOI Listing

Publication Analysis

Top Keywords

geometric problems
12
area universality
12
complexity class
8
deals existentially
8
existentially quantified
8
quantified variables
8
problems
6
completeness complexity
4
class area-universality
4
area-universality exhibiting
4

Similar Publications

Background: Diffusion-weighted (DW) turbo-spin-echo (TSE) imaging offers improved geometric fidelity compared to single-shot echo-planar-imaging (EPI). However, it suffers from low signal-to-noise ratio (SNR) and prolonged acquisition times, thereby restricting its applications in diagnosis and MRI-guided radiotherapy (MRgRT).

Purpose: To develop a joint k-b space reconstruction algorithm for concurrent reconstruction of DW-TSE images and the apparent diffusion coefficient (ADC) map with enhanced image quality and more accurate quantitative measurements.

View Article and Find Full Text PDF

Coupled cluster theory in the standard formulation is unable to correctly describe conical intersections among states of the same symmetry. This limitation has restricted the practical application of an otherwise highly accurate electronic structure model, particularly in nonadiabatic dynamics. Recently, the intersection problem among the excited states was fully characterized and resolved.

View Article and Find Full Text PDF

Fractal Conditional Correlation Dimension Infers Complex Causal Networks.

Entropy (Basel)

November 2024

Department of Electrical and Computer Engineering, Clarkson University, 8 Clarkson Ave., Potsdam, NY 13699, USA.

Determining causal inference has become popular in physical and engineering applications. While the problem has immense challenges, it provides a way to model the complex networks by observing the time series. In this paper, we present the optimal conditional correlation dimensional geometric information flow principle (oGeoC) that can reveal direct and indirect causal relations in a network through geometric interpretations.

View Article and Find Full Text PDF

C-parameter version of robust bounded one-class support vector classification.

Sci Rep

January 2025

College of Mathematics and Systems Science, Xinjiang University, Urumqi , 830046, China.

ν-one-class support vector classification (ν-OCSVC) has garnered significant attention for its remarkable performance in handling single-class classification and anomaly detection. Nonetheless, the model does not yield a unique decision boundary, and potentially compromises learning performance when the training data is contaminated by some outliers or mislabeled observations. This paper presents a novel C-parameter version of bounded one-class support vector classification (C-BOCSVC) to determine a unique decision boundary.

View Article and Find Full Text PDF

A data transmission delay compensation algorithm for an interactive communication network of an offshore oil field operation scene in severe weather is proposed. To solve the problem of unstable microwave signals and a large amount of noise in the communication network caused by bad weather, the communication network signal denoising method based on Lagrange multiplier symplectic singular value mode decomposition is adopted, and the communication network data denoising process is realized through five steps; phase space reconstruction, symplectic geometric similarity transformation, grouping, diagonal averaging, and adaptive reconstruction. Simultaneously, the weak communication signal is compensated after being captured, that is, the characteristics of the weak signal are enhanced.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!