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This paper describes a flexible and tractable bottom-up dynamic correlation modelling framework with a consistent stochastic recovery specification. The stochastic recovery specification only models the first two moments of the spot…

Pricing of Securities · Quantitative Finance 2010-04-22 Yadong Li

While modern representation learning relies heavily on global error signals, decentralized algorithms driven by local interactions offer a fundamental distributed alternative. However, the macroscopic convergence properties of these…

Machine Learning · Computer Science 2026-04-21 Zilin Li , Weiwei Xu , Xuchun Tong , Xuanbo Lu , Xuanqi Zhao

Learning dynamics from dissipative chaotic systems is notoriously difficult due to their inherent instability, as formalized by their positive Lyapunov exponents, which exponentially amplify errors in the learned dynamics. However, many of…

Machine Learning · Computer Science 2024-06-07 Yair Schiff , Zhong Yi Wan , Jeffrey B. Parker , Stephan Hoyer , Volodymyr Kuleshov , Fei Sha , Leonardo Zepeda-Núñez

Dynamical systems methods are used to investigate global behavior of the spatially flat Friedmann-Robertson-Walker cosmological model in gravitational theory with a non-minimally coupled scalar field and a constant potential function. We…

General Relativity and Quantum Cosmology · Physics 2015-11-11 Orest Hrycyna , Marek Szydlowski

The parametrisation method for invariant manifolds is a powerful technique for deriving reduced-order models in the context of nonlinear vibrating systems, allowing accurate computations of nonlinear normal modes. Thanks to arbitrary order…

Numerical Analysis · Mathematics 2026-03-19 André de Figueiredo Stabile , Aurélien Grolet , Alessandra Vizzaccaro , Cyril Touzé

In this paper we consider new regularization methods for linear inverse problems of dynamic type. These methods are based on dynamic programming techniques for linear quadratic optimal control problems. Two different approaches are…

Numerical Analysis · Mathematics 2021-01-26 S. Kindermann , A. Leitao

We obtain new types of exponential decay laws for solutions of density-matrix master equations in the weak-coupling limit: after comparing with results already present in the literature and developing the necessary techniques, we study the…

Quantum Physics · Physics 2015-05-13 David Taj , Fausto Rossi

This paper is concerned with stability analysis and synthesis for discrete-time linear systems with stochastic dynamics. Equivalence is first proved for three stability notions under some key assumptions on the randomness behind the…

Systems and Control · Computer Science 2019-03-01 Yohei Hosoe , Tomomichi Hagiwara

This paper studies the bank dynamic decision problem in the intermediate time step for a discrete-time setup. We have considered a three-time-step model. Initially, the banks raise money through debt and equity and invest in different types…

Risk Management · Quantitative Finance 2025-01-15 Deb Narayan Barik , Siddhartha P. Chakrabarty

In recent years, various means of efficiently detecting changepoints in the univariate setting have been proposed, with one popular approach involving minimising a penalised cost function using dynamic programming. In some situations, these…

Methodology · Statistics 2018-10-09 S. O. Tickle , I. A. Eckley , P. Fearnhead , K. Haynes

In this short paper, we consider discrete-time Markov chains on lattices as approximations to continuous-time diffusion processes. The approximations can be interpreted as finite difference schemes for the generator of the process. We…

Probability · Mathematics 2016-11-08 Christoph Reisinger

Experiments and computer simulation studies have revealed existence of rich dynamics in the orientational relaxation of molecules in confined systems such as water in reverse micelles, cyclodextrin cavities and nano-tubes. Here we introduce…

Statistical Mechanics · Physics 2015-05-18 Rajib Biswas , Biman Bagchi

Gibbs sampling is a Markov Chain Monte Carlo (MCMC) method often used in Bayesian learning. MCMC methods can be difficult to deploy on parallel and distributed systems due to their inherently sequential nature. We study asynchronous Gibbs…

Computation · Statistics 2020-03-03 Alexander Terenin , Daniel Simpson , David Draper

We study approximations of evolving probability measures by an interacting particle system. The particle system dynamics is a combination of independent Markov chain moves and importance sampling/resampling steps. Under global regularity…

Probability · Mathematics 2011-12-12 Andreas Eberle , Carlo Marinelli

We introduce an extension of finite mixture models by incorporating skew-normal distributions within a Hidden Markov Model framework. By assuming a constant transition probability matrix and allowing emission distributions to vary according…

Methodology · Statistics 2025-09-25 Andrea Nigri , Marco Forti , Han Lin Shang

This paper focuses on linearisation techniques for a class of mixed singular/continuous control problems and ensuing algorithms. The motivation comes from (re)insurance problems with reserve-dependent premiums with Cram{\'e}r-Lundberg…

Optimization and Control · Mathematics 2022-06-22 Dan Goreac , Juan Li , Boxiang Xu

This paper is concerned with finite-level quantum memory systems for retaining initial dynamic variables in the presence of external quantum noise. The system variables have an algebraic structure, similar to that of the Pauli matrices, and…

Optimization and Control · Mathematics 2026-04-01 Igor G. Vladimirov , Ian R. Petersen , Guodong Shi

Using a pair of coupled LRC cavities we experimentally demonstrate that instabilities and amplification action can be tamed by a spatially inhomogenous gain. Specifically we observe the counter-intuitive phenomenon of stabilization of the…

Optics · Physics 2013-09-17 M. Chitsazi , S. Factor , J. Schindler , H. Ramezani , F. M. Ellis , T. Kottos

In this work we numerically investigate a new method for the characterization of growing length scales associated with spatially heterogeneous dynamics of glass-forming liquids. This approach, motivated by the formulation of the…

Disordered Systems and Neural Networks · Physics 2013-10-28 Kang Kim , Shinji Saito , Kunimasa Miyazaki , Giulio Biroli , David R. Reichman

The robustness of dynamical systems against external perturbations is crucial in engineering; however, it is often overlooked for the lack of methods for rapidly computing it. This paper proposes a novel algorithm for estimating the…

Dynamical Systems · Mathematics 2023-06-27 Bence Szaksz , Gabor Stepan , Giuseppe Habib