Results 81 to 90 of about 103,412 (240)
Operator-Lipschitz functions in Schatten–von Neumann classes
Acta Mathematica, 2011 This paper resolves a number of conjectures in the perturbation theory of linear operators. Namely, we prove that every Lipschitz function is operator Lipschitz in the Schatten-von Neumann ideals $S^ $, $1 < < \infty$. The negative result for $S^ $, $ = 1, \infty$ was earlier established by Yu. Farforovskaya in 1972.
Potapov, Denis, Sukochev, Fedor
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CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT A formation inversion algorithm with real‐time performance and accuracy is crucial for natural gamma logging while drilling (LWD). However, traditional inversion algorithms are often limited by high computational resource consumption and insufficient accuracy.
Juntao Liu +4 more
wiley +1 more source
Fixed Point Theory and Applications, 2007 Let E be an arbitrary real Banach space and K a nonempty, closed, convex (not necessarily bounded) subset of E. If T is a member of the class of Lipschitz, strongly pseudocontractive maps with Lipschitz constant L≥1, then it is shown that to each ...
K. N. V. V. Vara Prasad, G. V. R. Babu
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CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT This paper presents HealthNet, a novel framework for the dynamic optimisation of healthcare transportation networks using multi‐agent reinforcement learning. HealthNet leverages a spatiotemporal dependency module to capture complex spatiotemporal relationships in healthcare demand and resource allocation patterns, combined with centralised ...
Jianhui Lv +3 more
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On projections of metric spaces
Journal of Computational Geometry, 2014 Let $X$ be a metric space and let $\mu$ be a probability measure on it. Consider a Lipschitz map $T: X \rightarrow \mathbb{R}^n$, with Lipschitz constant $\leq 1$. Then one can ask whether the image $TX$ can have large projections on many directions. For
Mark Kozdoba
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SDFs from Unoriented Point Clouds using Neural Variational Heat Distances
Computer Graphics Forum, EarlyView.We propose a novel variational approach for computing neural Signed Distance Fields (SDF) from unoriented point clouds. We first compute a small time step of heat flow (middle) and then use its gradient directions to solve for a neural SDF (right). Abstract We propose a novel variational approach for computing neural Signed Distance Fields (SDF) from ...
Samuel Weidemaier +5 more
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Integrating Lipschitz Extensions and Probabilistic Modelling for Metric Space Classification
MathematicsLipschitz-based classification provides a flexible framework for general metric spaces, naturally adapting to complex data structures without assuming linearity.
Roger Arnau +2 more
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On finite lipschitz Orlicz-Sobolev classes
Владикавказский математический журнал, 2015 Найдено достаточное условие конечной липшицевости гомеоморфизмов класса Орлича - Соболева W1,φloc при наличии условия типа Кальдерона на φ.
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On Metric Choice in Dimension Reduction for Fréchet Regression
International Statistical Review, EarlyView.Summary Fréchet regression is becoming a mainstay in modern data analysis for analysing non‐traditional data types belonging to general metric spaces. This novel regression method is especially useful in the analysis of complex health data such as continuous monitoring and imaging data.
Abdul‐Nasah Soale +3 more
wiley +1 more source
A Comparative Review of Specification Tests for Diffusion Models
International Statistical Review, EarlyView.Summary Diffusion models play an essential role in modelling continuous‐time stochastic processes in the financial field. Therefore, several proposals have been developed in the last decades to test the specification of stochastic differential equations.
A. López‐Pérez +3 more
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