Tootfinder

Adam Sandler raises stakes with Derrick Henry, 2,000-yard movie bet

https://www.cbssports.com/nfl/news/adam-sandler-raises-stakes-with-derrick-henry-2000…

Achieving Linear Speedup and Near-Optimal Complexity for Decentralized Optimization over Row-stochastic Networks
Liyuan Liang, Xinyi Chen, Gan Luo, Kun Yuan
https://arxiv.org/abs/2506.04600

Achieving Linear Speedup and Near-Optimal Complexity for Decentralized Optimization over Row-stochastic Networks
A key challenge in decentralized optimization is determining the optimal convergence rate and designing algorithms to achieve it. While this problem has been extensively addressed for doubly-stochastic and column-stochastic mixing matrices, the row-stochastic scenario remains unexplored. This paper bridges this gap by introducing effective metrics to capture the influence of row-stochastic mixing matrices and establishing the first convergence lower bound for decentralized learning over row-sto…

An Augmented Lagrangian Preconditioner for Navier--Stokes Equations with Runge--Kutta in Time
Santolo Leveque, Yunhui He, Maxim Olshanskii
https://arxiv.org/abs/2506.04451

An Augmented Lagrangian Preconditioner for Navier--Stokes Equations with Runge--Kutta in Time
We consider a Runge--Kutta method for the numerical time integration of the nonstationary incompressible Navier--Stokes equations. This yields a sequence of nonlinear problems to be solved for the stages of the Runge--Kutta method. The resulting nonlinear system of differential equations is discretized using a finite element method. To compute a numerical approximation of the stages at each time step, we employ Newton's method, which requires the solution of a large and sparse generalized saddl…

Online matching on stochastic block model
Maria Cherifa (MAP5, CREST), Cl\'ement Calauz\`enes (CREST), Vianney Perchet (CREST)
https://arxiv.org/abs/2506.04921

Online matching on stochastic block model
While online bipartite matching has gained significant attention in recent years, existing analyses in stochastic settings fail to capture the performance of algorithms on heterogeneous graphs, such as those incorporating inter-group affinities or other social network structures. In this work, we address this gap by studying online bipartite matching within the stochastic block model (SBM). A fixed set of offline nodes is matched to a stream of online arrivals, with connections governed probabi…

Stochastic thermodynamics for classical non-Markov jump processes
Kiyoshi Kanazawa, Andreas Dechant
https://arxiv.org/abs/2506.04726 https://

Stochastic thermodynamics for classical non-Markov jump processes
Stochastic thermodynamics investigates energetic/entropic bounds in small systems, such as biomolecular motors, chemical-reaction networks, and quantum nano-devices. Foundational results, including the second law and thermodynamic uncertainty relations, predominantly rely on the Markov assumption -- neglecting history dependence of physical systems. However, while physicists recognise that the Markov assumption is dubious in real experimental setups, extending stochastic thermodynamics to gener…

This https://arxiv.org/abs/2408.11315 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Smoothing Variances Across Time: Adaptive Stochastic Volatility
We introduce a novel Bayesian framework for estimating time-varying volatility by extending the Random Walk Stochastic Volatility (RWSV) model with Dynamic Shrinkage Processes (DSP) in log-variances. Unlike the classical Stochastic Volatility (SV) or GARCH-type models with restrictive parametric stationarity assumptions, our proposed Adaptive Stochastic Volatility (ASV) model provides smooth yet dynamically adaptive estimates of evolving volatility and its uncertainty. We further enhance the mo…

Should the Raiders Consider Adding This Veteran Free Agent? https://www.si.com/nfl/raiders/las-vegas-stephon-gilmore-pete-carroll-heremy-chin-eric-stokes

Lions and Muons: Optimization via Stochastic Frank-Wolfe
Maria-Eleni Sfyraki, Jun-Kun Wang
https://arxiv.org/abs/2506.04192 https://a…

Lions and Muons: Optimization via Stochastic Frank-Wolfe
Stochastic Frank-Wolfe is a classical optimization method for solving constrained optimization problems. On the other hand, recent optimizers such as Lion and Muon have gained quite significant popularity in deep learning. In this work, we provide a unifying perspective by interpreting these seemingly disparate methods through the lens of Stochastic Frank-Wolfe. Specifically, we show that Lion and Muon with weight decay can be viewed as special instances of a Stochastic Frank-Wolfe, and we esta…

A framework for fluctuating times and counting observables in stochastic excursions
Guilherme Fiusa, Pedro E. Harunari, Abhaya S. Hegde, Gabriel T. Landi
https://arxiv.org/abs/2506.05160

A framework for fluctuating times and counting observables in stochastic excursions
Many natural systems exhibit dynamics characterized by alternating phases. Describing the fluctuations of such systems over stochastic trajectories is necessary across diverse fields, from biological motors to quantum thermal machines. In an accompanying Letter, we introduced the notion of stochastic excursions -- a framework to analyze far from equilibrium processes via sub-trajectories. Through counting observables, this framework captures finite-time fluctuations and trajectory-level behavio…

This https://arxiv.org/abs/2407.04562 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

An SDE Perspective on Stochastic Inertial Gradient Dynamics with Time-Dependent Viscosity and Geometric Damping
Our approach is part of the close link between continuous dissipative dynamical systems and optimization algorithms. We aim to solve convex minimization problems by means of stochastic inertial differential equations which are driven by the gradient of the objective function. This will provide a general mathematical framework for analyzing fast optimization algorithms with stochastic gradient input. Our study is a natural extension of our previous work devoted to the first-order in time stochas…