Tootfinder

🔊 #NowPlaying on BBCRadio2's #JoWhiley
Coldplay:
🎵 We Never Change
#Coldplay
https://open.spotify.com/track/5TB6QgrF0RPIxSCGfRDLoe
https://theglumsters.bandcamp.com/track/we-never-change

Anna-Theresa Korbutt - Die Jeanne d’Arc des ÖPNV
Veränderungen in der deutschen Mobilitätslandschaft herbeizuführen, kommt einem Kampf gegen Windmühlen gleich. Wieso HVV-Chefin Anna-Theresa Korbutt alles dafür mitbringt, es doch zu versuchen, erfahren Sie in der neuen Folge von WAS MICH BEWEGT.

Anna-Theresa Korbutt: Die Jeanne d’Arc des ÖPNV
Wieso HVV-Chefin Anna-Theresa Korbutt alles versucht, um Veränderungen in der deutschen Mobilitätslandschaft herbeizuführen, hören Sie hier.

If a #Labour government did abolish the #TwoChildCap across the UK, what consequences would that have for the #Scottish budget? This document suggests that the whole benefit would be passed on to families…

Child poverty cumulative impact assessment: update
This report estimates the impact of Scottish Government policies on child poverty, updating the modelling that was originally undertaken for the second Tackling Child Poverty Delivery Plan.

Anna-Theresa Korbutt - Die Jeanne d’Arc des ÖPNV
Veränderungen in der deutschen Mobilitätslandschaft herbeizuführen, kommt einem Kampf gegen Windmühlen gleich. Wieso HVV-Chefin Anna-Theresa Korbutt alles dafür mitbringt, es doch zu versuchen, erfahren Sie in der neuen Folge von WAS MICH BEWEGT.

Anna-Theresa Korbutt: Die Jeanne d’Arc des ÖPNV
Wieso HVV-Chefin Anna-Theresa Korbutt alles versucht, um Veränderungen in der deutschen Mobilitätslandschaft herbeizuführen, hören Sie hier.

Der Übergang zu erneuerbaren Energien wird durch die politische Macht der fossilen #Brennstoffindustrie behindert. Obwohl #Solarenergie und #Windenergie oft günstiger sind als

Fossil fuels are shredding our democracy
Their goal is to stop us from switching to clean energy

Block Coordinate Descent Methods for Optimization under J-Orthogonality Constraints with Applications
Di He, Ganzhao Yuan, Xiao Wang, Pengxiang Xu
https://arxiv.org/abs/2406.09771 https://arxiv.org/pdf/2406.09771
arXiv:2406.09771v1 Announce Type: new
Abstract: The J-orthogonal matrix, also referred to as the hyperbolic orthogonal matrix, is a class of special orthogonal matrix in hyperbolic space, notable for its advantageous properties. These matrices are integral to optimization under J-orthogonal constraints, which have widespread applications in statistical learning and data science. However, addressing these problems is generally challenging due to their non-convex nature and the computational intensity of the constraints. Currently, algorithms for tackling these challenges are limited. This paper introduces JOBCD, a novel Block Coordinate Descent method designed to address optimizations with J-orthogonality constraints. We explore two specific variants of JOBCD: one based on a Gauss-Seidel strategy (GS-JOBCD), the other on a variance-reduced and Jacobi strategy (VR-J-JOBCD). Notably, leveraging the parallel framework of a Jacobi strategy, VR-J-JOBCD integrates variance reduction techniques to decrease oracle complexity in the minimization of finite-sum functions. For both GS-JOBCD and VR-J-JOBCD, we establish the oracle complexity under mild conditions and strong limit-point convergence results under the Kurdyka-Lojasiewicz inequality. To demonstrate the effectiveness of our method, we conduct experiments on hyperbolic eigenvalue problems, hyperbolic structural probe problems, and the ultrahyperbolic knowledge graph embedding problem. Extensive experiments using both real-world and synthetic data demonstrate that JOBCD consistently outperforms state-of-the-art solutions, by large margins.