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By considering the master equation of the totally asymmetric exclusion process on a one-dimensional lattice and using two types of boundary conditions (i.e. interactions), two new families of the multi-species reaction-diffusion processes,…

Statistical Mechanics · Physics 2013-01-15 Yaghoob Naimi , Frinaz Roshani

We present a novel method for approximately equilibrating a matrix $A \in {\bf R}^{m \times n}$ using only multiplication by $A$ and $A^T$. Our method is based on convex optimization and projected stochastic gradient descent, using an…

Optimization and Control · Mathematics 2016-02-23 Steven Diamond , Stephen Boyd

In many distributed learning problems, the heterogeneous loading of computing machines may harm the overall performance of synchronous strategies. In this paper, we propose an effective asynchronous distributed framework for the…

Machine Learning · Statistics 2017-05-23 Bikash Joshi , Franck Iutzeler , Massih-Reza Amini

Totally asymmetric simple exclusion processes on lattices with junctions, where particles interact with hard-core exclusion and move on parallel lattice branches that at the junction combine into a single lattice segment, are investigated.…

Statistical Mechanics · Physics 2009-11-11 Ekaterina Pronina , Anatoly B. Kolomeisky

An extension of the totally asymmetric exclusion process, which incorporates a dynamically extending lattice is explored. Although originally inspired as a model for filamentous fungal growth, here the dynamically extending exclusion…

Statistical Mechanics · Physics 2007-12-10 K. E. P. Sugden , M. R. Evans

We formulate a new integrable asymmetric exclusion process with $N-1=0,1,2,...$ kinds of impurities and with hierarchically ordered dynamics. The model we proposed displays the full spectrum of the simple asymmetric exclusion model plus new…

Statistical Mechanics · Physics 2015-06-05 Matheus J. Lazo , Anderson A. Ferreira

Recent work has established an empirically successful framework for adapting learning rates for stochastic gradient descent (SGD). This effectively removes all needs for tuning, while automatically reducing learning rates over time on…

Machine Learning · Computer Science 2013-03-28 Tom Schaul , Yann LeCun

This paper proposes new parametric model adequacy tests for possibly nonlinear and nonstationary time series models with noncontinuous data distribution, which is often the case in applied work. In particular, we consider the correct…

Statistics Theory · Mathematics 2021-08-10 Igor Kheifets , Carlos Velasco

We study the block-coordinate forward-backward algorithm in which the blocks are updated in a random and possibly parallel manner, according to arbitrary probabilities. The algorithm allows different stepsizes along the block-coordinates to…

Optimization and Control · Mathematics 2020-11-30 Saverio Salzo , Silvia Villa

In a recent article the most general non-uniform reaction-diffusion models on a one-dimensional lattice with boundaries were considered, for which the time evolution equations of correlation functions are closed and the stationary profile…

Statistical Mechanics · Physics 2011-08-09 Amir Aghamohammadi , Mohammad Khorrami

We consider the asymmetric simple exclusion process (ASEP) with open boundary condition at the left boundary, where particles exit at rate {\gamma} and enter at rate {\alpha} = {\gamma}{\tau}^2, and where {\tau} is the asymmetry parameter…

Mathematical Physics · Physics 2020-01-01 Jeffrey Kuan

Previous studies have established that quasiperiodic lattice models with unbounded potentials can exhibit localized and multifractal states, yet preclude the existence of extended states. In this work, we introduce a quasiperiodic system…

Disordered Systems and Neural Networks · Physics 2025-02-20 Jia-Ming Zhang , Shan-Zhong Li , Shi-Liang Zhu , Zhi Li

We study the nonequilibrium steady states in totally asymmetric exclusion processes (TASEP) with open boundary conditions having spatially inhomogeneous hopping rates. Considering smoothly varying hopping rates, we show that the steady…

Statistical Mechanics · Physics 2023-01-03 Atri Goswami , Mainak Chatterjee , Sudip Mukherjee

The alternating gradient descent (AGD) is a simple but popular algorithm which has been applied to problems in optimization, machine learning, data ming, and signal processing, etc. The algorithm updates two blocks of variables in an…

Optimization and Control · Mathematics 2018-03-01 Songtao Lu , Mingyi Hong , Zhengdao Wang

We consider symmetric and Hermitian random matrices whose entries are independent and symmetric random variables with an arbitrary variance pattern. Under a novel Short-to-Long Mixing condition, which is sharp in the sense that it precludes…

Probability · Mathematics 2025-11-12 Dang-Zheng Liu , Guangyi Zou

By using a recently proposed probabilistic approach, we determine the exact ground state of a class of matrix Hamiltonian models characterized by the fact that in the thermodynamic limit the multiplicities of the potential values assumed by…

Disordered Systems and Neural Networks · Physics 2007-05-23 Massimo Ostilli , Carlo Presilla

This paper deals with the stabilization of a class of linear infinite-dimensional systems with unbounded control operators and subject to a boundary disturbance. We assume that there exists a linear feedback law that makes the origin of the…

Analysis of PDEs · Mathematics 2022-10-26 Ismaïla Balogoun , Swann Marx , Franck Plestan

This work is devoted to the development of a novel theoretical approach, named hybrid approach, to handle a localized bottleneck in a symmetrically coupled two-channel totally asymmetric simple exclusion process with Langmuir kinetics. The…

Computational Physics · Physics 2016-02-17 Isha Dhiman , Arvind Kumar Gupta

An adaptive mesh refinement (AMR) scheme is implemented in a distributed environment using Message Passing Interface (MPI) to find solutions to the nonlinear sigma model. Previous work studied behavior similar to black hole critical…

General Relativity and Quantum Cosmology · Physics 2015-06-25 Steven L. Liebling

We consider a generic convex-concave saddle point problem with separable structure, a form that covers a wide-ranged machine learning applications. Under this problem structure, we follow the framework of primal-dual updates for saddle…

Machine Learning · Statistics 2015-06-15 Zhanxing Zhu , Amos J. Storkey