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S-D-RSM: Stochastic Distributed Regularized Splitting Method for Large-Scale Convex Optimization Problems
Maoran Wang, Xingju Cai, Yongxin Chen
https://arxiv.org/abs/2511.10133 https://arxiv.org/pdf/2511.10133 https://arxiv.org/html/2511.10133
arXiv:2511.10133v1 Announce Type: new
Abstract: This paper investigates the problems large-scale distributed composite convex optimization, with motivations from a broad range of applications, including multi-agent systems, federated learning, smart grids, wireless sensor networks, compressed sensing, and so on. Stochastic gradient descent (SGD) and its variants are commonly employed to solve such problems. However, existing algorithms often rely on vanishing step sizes, strong convexity assumptions, or entail substantial computational overhead to ensure convergence or obtain favorable complexity. To bridge the gap between theory and practice, we integrate consensus optimization and operator splitting techniques (see Problem Reformulation) to develop a novel stochastic splitting algorithm, termed the \emph{stochastic distributed regularized splitting method} (S-D-RSM). In practice, S-D-RSM performs parallel updates of proximal mappings and gradient information for only a randomly selected subset of agents at each iteration. By introducing regularization terms, it effectively mitigates consensus discrepancies among distributed nodes. In contrast to conventional stochastic methods, our theoretical analysis establishes that S-D-RSM achieves global convergence without requiring diminishing step sizes or strong convexity assumptions. Furthermore, it achieves an iteration complexity of $\mathcal{O}(1/\epsilon)$ with respect to both the objective function value and the consensus error. Numerical experiments show that S-D-RSM achieves up to 2--3$\times$ speedup compared to state-of-the-art baselines, while maintaining comparable or better accuracy. These results not only validate the algorithm's theoretical guarantees but also demonstrate its effectiveness in practical tasks such as compressed sensing and empirical risk minimization.
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Replaced article(s) found for math.SG. https://arxiv.org/list/math.SG/new
[1/1]:
- Non-decomposable Lagrangian cobordisms between Legendrian knots
Roman Golovko, Daniel Kom\'arek
https://arxiv.org/abs/2511.08731 https://mastoxiv.page/@arXiv_mathSG_bot/115541377678336506
- Spaces of Legendrian cables and Seifert fibered links
Eduardo Fern\'andez, Hyunki Min
https://arxiv.org/abs/2310.12385 https://mastoxiv.page/@arXiv_mathGT_bot/111265563434686287
- Almost Hermitian structures on virtual moduli spaces of non-Abelian monopoles and applications to...
Paul M. N. Feehan, Thomas G. Leness
https://arxiv.org/abs/2410.13809 https://mastoxiv.page/@arXiv_mathDG_bot/113327305560976416
- Quantum cohomology, shift operators, and Coulomb branches
Ki Fung Chan, Kwokwai Chan, Chin Hang Eddie Lam
https://arxiv.org/abs/2505.23340 https://mastoxiv.page/@arXiv_mathAG_bot/114595582234065991
- One application of Duistermaat-Heckman measure in quantum information theory
Lin Zhang, Xiaohan Jiang, Bing Xie
https://arxiv.org/abs/2507.02369 https://mastoxiv.page/@arXiv_quantph_bot/114794376818737255
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But the debt industry itself hasn't gotten any more efficient: "the cost of moving a dollar from a saver to a borrower was about two cents in 1910; a hundred years later, it was the same." ... "This puzzle resolves itself once we recognize that the vast majority of financial innovation is geared towards figuring out how to siphon off resources through fees, insider information and lobbying."

Real conspiracies tend to come out, but some of them take a while. Information on the Iran/Contra scandal broke out about 5 years after the conspiracy started. That would have taken several hundred people to carry out, so it was somewhat hard to hide. Even so, they largely got away with it.
The moon landing conspiracy theory would have taken thousands of people, so it would have come out more quickly. Since we have an example of a real secret program of a similar scale as what would be required to fake a moon landing (that is, the Manhattan project), we know that the fake moon landing conspiracy theory is not true. (There's also the literally tons of evidence in the form of rocks and other samples, and all kinds of other ways to debunk the claim.)
Could Kash Patel's FBI have been trying really hard to entrap people into carrying out terrorist attacks in order to justify #Trump's occupation of DC? Could they have helped a guy plan an attack then just failed to arrest him? There are reasonable scenarios that fall in between malice and incompetence while still indicating some level of false flag.
Could someone have just snapped and ambushed some guardsmen without any involvement from the FBI? Yeah, totally. The US is a country full of guns with a completely non-functional mental health system. Someone coming from a country that the US destroyed, twice, could have a lot of untreated trauma. Might they see the national guard as a threat (even if that wasn't totally true)? Yeah, they were deployed to threaten people (even when they were just picking up trash). The point was to incite this kind of response. It's completely reasonable to believe that the FBI would not need to be involved at all, that this would just be the stochastic response they were looking for.
So the point here is that everything is on the table, nothing is really known, nothing should be surprising, and no matter what it's Trump's fault. This is exactly the escalation he was looking for. If he didn't get it naturally, he would also have had ways of making it happen.
He will use this in exactly the same way as the Reichstag fire, to drive a wedge between liberals and radicals. Don't fall for it.
Edit:
There are plausible reasons to not believe the official narrative at all right now, or maybe ever. The official narrative is also plausible, but there are plausible reasons to disagree with the response even if the official story is true. It is unnecessary to resort to conspiracy thinking in order to account for what happened and to disagree with the response. But it is also understandable why someone might jump immediately to a conspiracy given the circumstances.

istg people who have opinions on AGI should be required by law to at least grasp the basics of information theory

Infinite-dimensional Lagrange-Dirac systems with boundary energy flow II: Field theories with bundle-valued forms
Fran\c{c}ois Gay-Balmaz, \'Alvaro Rodr\'iguez Abella, Hiroaki Yoshimura
https://arxiv.org/abs/2511.05687 https://arxiv.org/pdf/2511.05687 https://arxiv.org/html/2511.05687
arXiv:2511.05687v1 Announce Type: new
Abstract: Part I of this paper introduced the infinite dimensional Lagrange--Dirac theory for physical systems on the space of differential forms over a smooth manifold with boundary. This approach is particularly well-suited for systems involving energy exchange through the boundary, as it is built upon a restricted dual space -a vector subspace of the topological dual of the configuration space- that captures information about both the interior dynamics and boundary interactions. Consequently, the resulting dynamical equations naturally incorporate boundary energy flow. In this second part, the theory is extended to encompass vector-bundle-valued differential forms and non-Abelian gauge theories. To account for two commonly used forms of energy flux and boundary power densities, we introduce two distinct but equivalent formulations of the restricted dual. The results are derived from both geometric and variational viewpoints and are illustrated through applications to matter and gauge field theories. The interaction between gauge and matter fields is also addressed, along with the associated boundary conditions, applied to the case of the Yang-Mills-Higgs equations.
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Series D, Episode 09 - Sand
SOOLIN: All right Avon, you've told us your theory. It wasn't a plague on Virn; it was the sand feeding off human energy. Does that mean we reckon Tarrant is dead?
AVON: Not necessarily. According to Orac's earlier information there's probably a woman down there. It's just possible that Tarrant was stronger and fitter than the three men in the Federation patrol, in which case he may have been kept alive, as Keller was kept alive, a…

RE: https://fediscience.org/@suomenantropologi/115614886106180185
Vale Keith Hart. That keynote from 2010 (link below) was (and still is) a compelling argument for reconnecting value theory to practical political economy by weaving together Marx's commodity fetishism, the concept of plural economy inspired by Mauss, and the digital revolution's radical cheapening of information.

Can You Hear Me Now? A Benchmark for Long-Range Graph Propagation
Luca Miglior, Matteo Tolloso, Alessio Gravina, Davide Bacciu
https://arxiv.org/abs/2512.17762 https://arxiv.org/pdf/2512.17762 https://arxiv.org/html/2512.17762
arXiv:2512.17762v1 Announce Type: new
Abstract: Effectively capturing long-range interactions remains a fundamental yet unresolved challenge in graph neural network (GNN) research, critical for applications across diverse fields of science. To systematically address this, we introduce ECHO (Evaluating Communication over long HOps), a novel benchmark specifically designed to rigorously assess the capabilities of GNNs in handling very long-range graph propagation. ECHO includes three synthetic graph tasks, namely single-source shortest paths, node eccentricity, and graph diameter, each constructed over diverse and structurally challenging topologies intentionally designed to introduce significant information bottlenecks. ECHO also includes two real-world datasets, ECHO-Charge and ECHO-Energy, which define chemically grounded benchmarks for predicting atomic partial charges and molecular total energies, respectively, with reference computations obtained at the density functional theory (DFT) level. Both tasks inherently depend on capturing complex long-range molecular interactions. Our extensive benchmarking of popular GNN architectures reveals clear performance gaps, emphasizing the difficulty of true long-range propagation and highlighting design choices capable of overcoming inherent limitations. ECHO thereby sets a new standard for evaluating long-range information propagation, also providing a compelling example for its need in AI for science.
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