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Fitting probabilistic models to data is often difficult, due to the general intractability of the partition function and its derivatives. Here we propose a new parameter estimation technique that does not require computing an intractable…

Machine Learning · Computer Science 2015-03-13 Jascha Sohl-Dickstein , Peter Battaglino , Michael R. DeWeese

We consider linear dynamical systems under floating-point rounding. In these systems, a matrix is repeatedly applied to a vector, but the numbers are rounded into floating-point representation after each step (i.e., stored as a…

Logic in Computer Science · Computer Science 2023-01-30 Engel Lefaucheux , Joël Ouaknine , David Purser , Mohammadamin Sharifi

The strict connection between Lie point-symmetries of a dynamical system and its constants of motion is discussed and emphasized, through old and new results. It is shown in particular how the knowledge of a symmetry of a dynamical system…

Mathematical Physics · Physics 2015-06-16 Giampaolo Cicogna

The dynamics of non-equilibrium Ising model with parallel updates is investigated using a generalized mean field approximation that incorporates multiple two-site correlations at any two time steps, which can be obtained recursively. The…

Disordered Systems and Neural Networks · Physics 2015-06-17 Hamed Mahmoudi , David Saad

We discuss analytical approximation schemes for the dynamics of diluted spin models. The original dynamics of the complete set of degrees of freedom is replaced by a hierarchy of equations including an increasing number of global…

Statistical Mechanics · Physics 2009-11-10 Guilhem Semerjian , Martin Weigt

We present a new method to close the Master Equation representing the continuous time dynamics of Ising interacting spins. The method makes use of the the theory of Random Point Processes to derive a master equation for local conditional…

Disordered Systems and Neural Networks · Physics 2017-05-17 Erik Aurell , Gino Del Ferraro , Eduardo Dominguez , Roberto Mulet

This paper compiles several aspects of the dynamics of stochastic approximation algorithms with Markov iterate-dependent noise when the iterates are not known to be stable beforehand. We achieve the same by extending the lock-in probability…

Dynamical Systems · Mathematics 2019-02-22 Prasenjit Karmakar , Shalabh Bhatnagar

We present the first exact asymptotic characterization of sequential dynamics for a broad class of local update algorithms on the Sherrington-Kirkpatrick (SK) model with Ising spins. Focusing on dynamics implemented via systematic scan --…

Disordered Systems and Neural Networks · Physics 2025-06-13 Yatin Dandi , David Gamarnik , Francisco Pernice , Lenka Zdeborová

We derive and study dynamical TAP equations for Ising spin glasses obeying both synchronous and asynchronous dynamics using a generating functional approach. The system can have an asymmetric coupling matrix, and the external fields can be…

Disordered Systems and Neural Networks · Physics 2011-05-17 Yasser Roudi , John Hertz

An implicit mass-matrix penalization (IMMP) of Hamiltonian dynamics is proposed, and associated dynamical integrators, as well as sampling Monte-Carlo schemes, are analyzed for systems with multiple time scales. The penalization is based on…

Numerical Analysis · Mathematics 2009-06-01 Petr Plechac , Mathias Rousset

In this paper we introduce an iterative Jacobi algorithm for solving distributed model predictive control (DMPC) problems, with linear coupled dynamics and convex coupled constraints. The algorithm guarantees stability and persistent…

Optimization and Control · Mathematics 2008-09-23 Dang Doan , Tamas Keviczky , Ion Necoara , Moritz Diehl

In the following, we discuss nonlinear simulations of nonlinear dynamical systems, which are applied in technical and biological models. We deal with different ideas to overcome the treatment of the nonlinearities and discuss a novel…

Numerical Analysis · Mathematics 2014-12-01 Juergen Geiser , Vahid Yaghoubi

Harnessing parallelism in seemingly sequential models is a central challenge for modern machine learning. Several approaches have been proposed for evaluating sequential processes in parallel using iterative fixed-point methods, like…

This paper studies the finite-time stability and stabilization of linear discrete time-varying stochastic systems with multiplicative noise. Firstly, necessary and sufficient conditions for finite-time stability are presented via state…

Optimization and Control · Mathematics 2018-06-25 Tianliang Zhang , Feiqi Deng , Weihai Zhang

The goal of the paper is to analytically examine escape probabilities for dynamical systems driven by symmetric $\alpha$-stable L\'evy motions. Since escape probabilities are solutions of a type of integro-differential equations (i.e.,…

Probability · Mathematics 2014-02-18 Huijie Qiao , Jinqiao Duan

The study presents a novel approach for stochastic nonlinear model updating in structural dynamics, employing a Bayesian framework integrated with Markov Chain Monte Carlo (MCMC) sampling for parameter estimation by using an approximated…

In this work we give a comprehensive overview of the time consistency property of dynamic risk and performance measures, focusing on a the discrete time setup. The two key operational concepts used throughout are the notion of the…

Mathematical Finance · Quantitative Finance 2017-01-31 Tomasz R. Bielecki , Igor Cialenco , Marcin Pitera

Much is now known about the consistency of Bayesian updating on infinite-dimensional parameter spaces with independent or Markovian data. Necessary conditions for consistency include the prior putting enough weight on the correct…

Statistics Theory · Mathematics 2022-03-18 Cosma Rohilla Shalizi

The approximation properties of a quadratic iso-parametric finite element method for a typical cavitation problem in nonlinear elasticity are analyzed. More precisely, (1) the finite element interpolation errors are established in terms of…

Numerical Analysis · Mathematics 2017-01-06 Chunmei Su , Zhiping Li

This paper presents a generalization of the sandpile model, called the parallel symmetric sandpile model, which inherits the rules of the symmetric sandpile model and implements them in parallel. In this new model, at each step the…

Discrete Mathematics · Computer Science 2012-07-04 E. Formenti , V. T. Pham , H. D. Phan , T. T. H. Tran