scispace - formally typeset
Search or ask a question

What is the Hahn-Banach extension theorem? 


Best insight from top research papers

The Hahn-Banach extension theorem is a fundamental result in functional analysis that deals with the extension of linear functionals defined on a subspace of a normed linear space to the entire space while preserving the norm of the functional. The theorem has various generalizations and applications in different areas of mathematics. The analytic form of the theorem, as proven by Paz, provides a slightly more general result in this direction . Olteanu discusses the extension results for linear operators and their applications to isotonicity of convex operators on a convex cone, as well as the use of the Krein-Milman theorem in representation theory and Choquet theory . Gutev relates the Hahn-Banach extension property to pointwise Lipschitz approximations of semi-continuous functions in a general metric domain . Niculescu and Olteanu characterize the isotonicity of continuous convex functions on the positive cone using subdifferentials . Fernández y Fernández-Arroyo presents a simple and direct proof of the geometric version of the Hahn-Banach theorem in the real case .

Answers from top 5 papers

More filters
Papers (5)Insight
The paper provides a proof of the Hahn-Banach theorem, but it does not explicitly define or explain what the Hahn-Banach extension theorem is.
The paper does not provide information about the Hahn-Banach extension theorem. The paper is about the characterization of the isotonicity of convex functions and the generalization of the Hardy-Littlewood-Polya inequality.
The paper does not provide information about the Hahn-Banach extension theorem.
Open accessJournal ArticleDOI
01 Jan 2022-Open Mathematics
6 Citations
The paper provides an overview of some Hahn-Banach type theorems, but does not explicitly state what the Hahn-Banach extension theorem is.
Book ChapterDOI
01 Jan 2023
The paper provides an analytic form of the Hahn-Banach theorem, which concerns the extension of linear functionals from a subspace to the entire space while preserving the norm of the functional.

Related Questions

Do Reverse hyper extensions cause hypertrophy?5 answersReverse hyperextensions have been shown to target the posterior chain musculature and can potentially lead to hypertrophy. In a study by Lawrence et al., it was found that increasing loads during reverse hyperextensions did not linearly increase force and muscle activation, suggesting that hypertrophy may not be directly proportional to the load used. Another study by Cuthbert et al. compared muscle activation during hyperextensions and reverse hyperextensions and found that the reverse hyperextension exercise resulted in significantly greater peak and mean electromyography (EMG) activity in the erector spinae, gluteus maximus, and biceps femoris muscles, indicating a higher intensity exercise for the posterior chain muscles. These findings suggest that reverse hyperextensions have the potential to induce hypertrophy in the targeted muscles.
What is extended definition?3 answersExtended definition refers to the broadening and expansion of the traditional definition of a concept or term. It involves encompassing additional aspects or dimensions that were not previously included in the original definition. In the context of the provided abstracts, extended definition is discussed in relation to memory and television transmission. In the field of neuroscience, memory is defined as the capacity to store and retrieve information, which includes both neuro-chemical processes in the brain and the potential for information storage outside of the brain. In the field of television transmission, extended definition involves the transmission of additional signals or information alongside the main signal, such as hidden video information or signals for extending the aspect ratio, horizontal and vertical definition of the picture. These examples highlight how extended definition allows for a more comprehensive understanding and application of concepts in different domains.
What are the advantages of brand extension?3 answersBrand extension offers several advantages for businesses. Firstly, it allows businesses to save costs by avoiding the need to create a new brand, as they can leverage the positive associations of an existing well-known brand. Secondly, brand extension can lead to increased brand awareness and visibility, as the new product benefits from the recognition and reputation of the parent brand. Thirdly, brand extension can result in economies of scale, as the business can leverage its existing distribution channels and customer base. Additionally, brand extension can prolong the brand's life cycle and increase brand equity. Finally, brand extension can lead to consumer acceptance, as consumers may already have a positive attitude towards the parent brand and are more likely to try the new product.
What is the proof for the above theorem?3 answersThe proof for the above theorem is provided by Taylor in 1983. Taylor's proof is based on lengthy calculations of involved distances and is summarized in the work. Chou also applied mechanical theorem proving methods to establish the theorem. Chou's computer proof took 44 hours of CPU time and involved manipulating huge polynomials. Refinements in algebraic methods have reduced the CPU time required for the proof. No elementary proof of the theorem has been published. The object of this paper is to provide an elementary proof of the theorem using Cartesian coordinate geometry. The principal algebraic difficulty lies in determining the roots of two quartic equations. Maple is used for this stage of the proof, but no advanced techniques are involved.
What is bernoulli theorem ?5 answersThe Bernoulli theorem is a fundamental principle in fluid mechanics that relates pressure, velocity, and elevation in a flowing fluid. It is based on the conservation of energy along a streamline. According to the theorem, as the velocity of a fluid increases, its pressure decreases, and vice versa. This principle has various applications in practical problems, such as hydraulic engineering and real-life scenarios. The Bernoulli theorem has been extended to flow in open channels, and a generalized depth-averaged Bernoulli theorem has been proposed. It has also been shown that the depth-averaged specific energy reaches a minimum in certain flow conditions. Understanding the Bernoulli principle is important in fluid mechanics and its applications in different fields.
What are the different types of quotients of Banach spaces?5 answersThere are different types of quotients of Banach spaces. One type is the separable quotient, which is a quotient space that is both infinite-dimensional and separable. This type of quotient has been proven to exist in various special cases, such as reflexive Banach spaces, weakly compactly generated (WCG) spaces, and dual spaces. Another type is the metrizable quotient, which is a quotient space that is infinite-dimensional and metrizable. It has been shown that the function space Cp(X) has an infinite-dimensional metrizable quotient when X either contains an infinite discrete C*-embedded subspace or has a sequence of infinite compact subsets with certain properties. These are two examples of the different types of quotients of Banach spaces.

See what other people are reading

Anything about black-box limitation and Maxent model?
5 answers
Black-box optimization methods face limitations when dealing with functions that are not Lipschitz smooth and strongly convex-concave around the optimal solution, leading to convergence issues. To address these challenges, innovative approaches like the evolution strategy (ES) combined with generative neural network (GNN) models have been proposed for black-box optimization in high-dimensional spaces, showcasing improved performance over traditional methods. Additionally, in the context of black-box targeted attacks, minimizing the maximum model discrepancy (M3D) among substitute models during the generation of adversarial examples enhances the attack success rate on black-box models, demonstrating superior performance compared to existing methods in various experiments. These advancements highlight the ongoing efforts to overcome limitations and enhance the effectiveness of black-box optimization and attack strategies.
What is the mathematical definition of infinity norm in the context of optimization algorithms?
4 answers
The infinity norm, denoted as $\ell_{\infty}$-norm, is a mathematical concept crucial in optimization algorithms. It is utilized in problems like $\ell_{\infty}$-norm minimization, which finds applications in various practical scenarios. The $\ell_{\infty}$-norm represents the maximum absolute value of a vector's components, providing a measure of its magnitude. In the context of optimization, the infinity norm plays a significant role in formulating efficient algorithms that aim to minimize this norm, often leading to improved convergence rates and solution accuracy. Algorithms leveraging the $\ell_{\infty}$-norm, such as those proposed for peak-to-average ratio reduction, demonstrate enhanced performance and reduced convergence time, especially when implemented on parallel hardware architectures.
How is the formula P = 1/2 * ρ * A * V^3 derived?
5 answers
The formula \( P = \frac{1}{2} \rho A V^3 \) is derived from a quantum mechanical description of a flat Friedmann-Robertson-Walker universe with the equation of state \( p = \rho \). This description corresponds to a dense black hole fluid, where the geometry features are obtained from purely quantum input. Additionally, in the context of MacDonald codes over finite non-chain rings, structural properties of MacDonald codes are studied, leading to the investigation of weight enumerators of torsion codes and applications in secret sharing schemes and association schemes. This interdisciplinary approach combines quantum mechanics with coding theory to derive and understand the formula \( P = \frac{1}{2} \rho A V^3 \).
What are the theoretical limitations of using a derivative-free method to solve nonlinear equations?
5 answers
The theoretical limitations of using derivative-free methods to solve nonlinear equations include challenges in determining the number of iterations needed to reach a certain error tolerance, uncertainty in isolating the solution within a specific region, and potential convergence even in cases not guaranteed by conditions. However, recent advancements have shown that conformable derivative-free methods, such as Steffensen's and Secant type methods, can offer numerical advantages over classical approaches, with improved convergence behavior and wider sets of initial estimations. Additionally, while derivative-free methods like the three-step iterative method based on Steffensen's method can achieve high convergence orders, they may still require multiple function evaluations per iteration, impacting computational efficiency. Despite these limitations, derivative-free methods can still be competitive with traditional approaches for solving large-scale nonlinear systems, showcasing superlinear convergence rates and effective global convergence.
What is proximity point in fixed point Theory?
5 answers
A proximity point in fixed point theory is a concept that extends the idea of a fixed point, particularly useful when dealing with non-self mappings where traditional fixed point theorems may not apply. It aims to find a point in a metric space that is closest to the images of multiple mappings, rather than just a single mapping. This notion becomes crucial in scenarios where traditional fixed point theorems fail to guarantee the existence of a fixed point for non-self mappings. The best proximity point theory merges metric fixed point theory and optimization theory, emphasizing the significance of finding the best possible solution in a given context.
How orthogonality defined in cvep bci papers?
5 answers
Orthogonality in the context of CVEP BCI papers is defined as a symmetric, irreflexive relation within a given set, often associated with complete ortholattices and posets. This concept is explored in various ways across different research papers. For instance, the notion of orthogonality spaces, which are sets equipped with such relations, is extensively studied in multiple papers. Additionally, the study of Birkhoff-James orthogonality of linear operators to subspaces of operators between Banach spaces is another facet of orthogonality analysis, particularly focusing on reflexive domains, finite-dimensional subspaces, and different norms in the context of Hilbert spaces. These diverse investigations shed light on the intricate nature of orthogonality within the realm of CVEP BCI research.
What is the relationship between Ricci curvature and Sasakian manifolds?
5 answers
The relationship between Ricci curvature and Sasakian manifolds is intricate and multifaceted. Sasakian manifolds can exhibit various structures related to Ricci curvature, such as almost ∗-Ricci solitons and *-conformal Ricci solitons. These structures play a crucial role in the geometric classification of Sasakian manifolds. Additionally, Sasakian lifts of K\"ahler manifolds inherit geometry closely related to their base, showcasing connections between Levi-Civita connections, curvatures, and Ricci tensors of the lift and the base manifold. The study of Sasakian manifolds with Ricci solitons provides insights into the geometric properties and classifications of these complex geometric spaces, highlighting the rich interplay between Sasakian geometry and Ricci curvature.
Is xgboost a multivariate data analysis tool?
5 answers
Yes, XGBoost can be utilized as a multivariate data analysis tool. The generalized XGBoost method proposed in one study allows for the use of more general loss functions, including non-convex ones, making it suitable for various applications requiring nonconvex loss functions. Additionally, an XGBoost-based model was developed to predict heat transfer coefficients in liquid cold plates, showcasing its effectiveness in analyzing multivariate data in thermal management solutions. Furthermore, a study on claim prediction in insurance demonstrated that XGBoost, a machine learning model based on decision trees, can handle big data efficiently by selecting subsets of features, making it scalable for multivariate analysis. These findings collectively highlight XGBoost's capability as a multivariate data analysis tool across different domains.
IJARST journal Q1 or Q2 ?
5 answers
The IJARST journal's categorization as Q1 or Q2 depends on various factors such as the research area, publication share, and citation characteristics. Publishing in Q1 journals is significant for performance-based funding, with Q1 publications receiving, on average, 2.07 times more citations than Q2 publications. The average share of documents published in Q1 across different research areas varies from 25.4% to 85.6%, with Q1 publications receiving 65% of total citations in the research area. Additionally, the significance of publishing in Q1 journals differs based on the research field, highlighting the importance of considering the specific area when evaluating the impact of publications.
What are the main applications of langevin models?
4 answers
The main applications of Langevin models include image synthesis, data denoising, sample restoration, outlier identification, Bayesian inference, and optimization in machine learning. Langevin models, particularly in the form of Stochastic Differential Equations, play a crucial role in Bayesian sampling algorithms and serve as a conceptual model for stochastic gradient flow in training over-parametrized models. These models have been utilized to enhance MCMC sampling of Energy-Based Models (EBMs) through approaches like Cyclical Stochastic Gradient Langevin Dynamics, which have shown improved efficiency in generating realistic images compared to traditional methods like Stochastic Gradient Langevin Dynamics (SGLD). Additionally, Langevin models have been applied in analyzing quantum electromagnetic systems with medium and radiation losses, enabling the study of quantum plasmonic phenomena and advancing quantum optics technology.
What is the formal and non-formal sphere in sport?
4 answers
Formal education in sports typically refers to structured learning within educational institutions like schools or sports academies, where coaching qualifications are obtained. On the other hand, non-formal education in sports involves informal learning and continuous professional development outside traditional educational settings. This includes activities like workshops, seminars, and self-directed learning that contribute to a coach's ongoing skill enhancement and knowledge acquisition. Research emphasizes the importance of non-formal education and informal learning for professional coaches, highlighting their role in skill development and career advancement. The success of coaches is influenced by a combination of formal training, natural talent, and ongoing non-formal and informal learning opportunities, showcasing the interconnected nature of education in the sports coaching profession.