scispace - formally typeset
Search or ask a question

Could you write a introduction about maxwell eigvalue optimization? 


Best insight from top research papers

Maxwell's eigenvalue problem is a topic of interest in optimization. Researchers have explored various discretization schemes and numerical methods to solve this problem efficiently. Kurz et al. developed a solution using isogeometric boundary elements and a contour integral method . They discussed the analytic properties of the discretization, outlined the implementation, and provided numerical examples. Boffi and Gastaldi proved the optimal convergence of an adaptive scheme based on edge finite elements for approximating the solutions of the eigenvalue problem associated with Maxwell's equations . Wang et al. explored efficient discretization schemes based on the parameterized approach and demonstrated the effectiveness of their algorithm through numerical experiments . These studies contribute to the understanding and optimization of Maxwell's eigenvalue problem.

Answers from top 5 papers

More filters
Papers (5)Insight
Book ChapterDOI
Anand J. Kulkarni, Kang Tai, Ajith Abraham 
01 Jan 2015
1 Citations
The given text does not provide any information about Maxwell eigvalue optimization.
This paper presents a method to solve Maxwell's eigenvalue problem using isogeometric boundary elements and a contour integral method.
Open accessJournal ArticleDOI
Wenjun Wang, Yu Zhang, Yidu Yang 
1 Citations
The paper explores efficient discretization schemes for the Maxwell eigenvalue problem and demonstrates their effectiveness through numerical experiments.
The paper discusses the optimal convergence of an adaptive finite element method for solving the eigenvalue problem associated with Maxwell's equations.
This paper discusses the solution of Maxwell's eigenvalue problem using isogeometric boundary elements and a contour integral method.

Related Questions

EM (Expectation Maximization ?5 answersThe Expectation-Maximization (EM) algorithm is widely utilized for computing maximum likelihood estimates, particularly in Gaussian Mixture Models (GMM). EM is valuable for clustering data based on GMM but can face challenges in low sample support scenarios where the covariance matrix updates become singular or poorly conditioned, impacting algorithm performance. To address this, a regularized version of the EM algorithm has been proposed, leveraging prior knowledge to enhance performance in small sample sizes. This regularization technique aims to ensure positive definiteness of covariance matrix updates by shrinking estimators towards specific target covariance matrices, ultimately improving the algorithm's stability and convergence in scenarios with limited data support.
How are optimization algorithms important to computing?4 answersOptimization algorithms are important to computing because they play a crucial role in improving the efficiency and performance of various applications and systems. These algorithms are widely used in fields such as system control, artificial intelligence, pattern recognition, and image processing. They help in achieving optimization in the production process, improving production efficiency, and saving resources. Optimization algorithms are also essential in machine learning and data analysis, where they serve as the solution engine for processing and interpreting large datasets. Additionally, optimization algorithms are a core subject in computer science education, as they teach students how to solve complex problems and find optimal solutions. Overall, optimization algorithms are instrumental in enhancing the performance, efficiency, and effectiveness of computing systems and applications.
What is search enginne optimization?4 answersSearch engine optimization (SEO) is the method of improving the visibility and popularity of a website or web page in the unpaid search results of a search engine. It involves understanding how search engines work, identifying relevant keywords, and making changes to the website's content and structure to increase its relevance to those keywords. SEO also includes strategies such as link building and promoting the website through various channels to increase its visibility on search engine results pages (SERPs). On-page optimization, which includes actions taken within web pages, and off-page optimization, which involves promoting the website through external factors, are both important aspects of SEO.
What is the content of the book An introduction to optimization techniques by sharma?5 answers"An Introduction to Optimization Techniques by Sharma provides a comprehensive introduction to the basic ideas and techniques of optimization. The book covers the process of optimization, including the formulation of optimization problems and the use of design constraints and criteria to find the optimal solution. It also discusses the assumptions and inner structure of the optimization methods, with real-world examples and applications provided throughout the book. The book is designed to be accessible to a broader readership, including students and professionals in science, engineering, and management programs. It includes learning outcomes, solved examples, and questions mapped to Bloom Taxonomy levels, as well as a separate chapter on the application of spreadsheets for solving optimization techniques."
What is Hyperparameter optimization?4 answersHyperparameter optimization is a technique used to improve the performance of machine learning models by tuning the hyperparameters. It involves selecting and configuring the hyperparameters to match the specific task at hand. Different optimization methods, such as Bayesian optimization and metaheuristic algorithms, can be used to find the optimal hyperparameter settings. Hyperparameter optimization has been applied to various domains, including spatial data analysis and numerical solution of differential equations. It has been shown to effectively improve the results of machine learning models and numerical simulation approaches. However, when training private ML models, privacy risks associated with hyperparameter optimization need to be considered. Differential privacy analysis has been used to develop a framework for adaptive private hyperparameter optimization, aiming to bridge the gap between private and non-private optimization methods.
What is search engine optimization?3 answersSearch Engine Optimization (SEO) is the process of increasing the visibility of a website in search engine result pages (SERPs) in order to attract more traffic and improve business. It involves using various techniques and tools to improve the ranking of a webpage on search engines like Google, Yahoo, and Bing. SEO helps websites appear at the top of search results when users search for specific keywords or phrases. By optimizing the content and structure of webpages, SEO aims to provide the most relevant and high-quality information to users. It also involves analyzing website performance using analytics tools to understand the audience better and make necessary improvements. SEO is an essential aspect of digital marketing and plays a crucial role in connecting businesses and individuals with their target audience.