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We study distributed stochastic convex optimization under the delayed gradient model where the server nodes perform parameter updates, while the worker nodes compute stochastic gradients. We discuss, analyze, and experiment with a setup…

Machine Learning · Statistics 2015-08-21 Suvrit Sra , Adams Wei Yu , Mu Li , Alexander J. Smola

We present a simulation-based inference approach for two-stage estimators, focusing on extremum estimators in the second stage. We accommodate a broad range of first-stage estimators, including extremum estimators, high-dimensional…

Econometrics · Economics 2024-11-08 Aristide Houndetoungan , Abdoul Haki Maoude

Two-step hybrid methods specially adapted to the numerical integration of perturbed oscillators are obtained. The formulation of the methods is based on a refinement of classical Taylor expansions due to Scheifele [{\em Z. Angew. Math.…

Numerical Analysis · Mathematics 2007-05-23 Hans Van de Vyver

By multiplexing information symbols in the delay-Doppler (DD) domain, orthogonal time frequency space (OTFS) is a promising candidate for future wireless communication in high-mobility scenarios. In addition to the superior communication…

Signal Processing · Electrical Eng. & Systems 2023-06-21 Kecheng Zhang , Zhongjie Li , Weijie Yuan , Yunlong Cai , Feifei Gao

In this paper, an original result in terms of a sufficient condition to test identifiability of nonlinear delayed-differential models with constant delays and multi-inputs is given. The identifiability is studied for the linearized system…

Dynamical Systems · Mathematics 2010-09-10 Carine Jauberthie , Louise Travé-Massuyès

The recent proposed orthogonal time frequency space (OTFS) modulation shows signifcant advantages than conventional orthogonal frequency division multiplexing (OFDM) for high mobility wireless communications. However, a challenging problem…

Information Theory · Computer Science 2022-07-25 Xiangxiang Li , Haiyan Wang , Yao Ge , Xiaohong Shen , Yuanyuan Lei

Differentiable simulators promise faster computation time for reinforcement learning by replacing zeroth-order gradient estimates of a stochastic objective with an estimate based on first-order gradients. However, it is yet unclear what…

Machine Learning · Computer Science 2022-08-23 H. J. Terry Suh , Max Simchowitz , Kaiqing Zhang , Russ Tedrake

The task of state estimation in active distribution systems faces a major challenge due to the integration of different measurements with multiple reporting rates. As a result, distribution systems are essentially unobservable in real time,…

Optimization and Control · Mathematics 2024-05-13 J. G. De la Varga , S. Pineda , J. M. Morales , Á. Porras

We show that, for appropriate combinations of the values of the delay and the forcing frequency, it is possible to obtain easily high-order averaged versions of periodically forced systems of delay differential equations with constant…

Dynamical Systems · Mathematics 2019-06-18 J. M. Sanz-Serna , Beibei Zhu

We consider the problem of estimating timing of measurements and actuation in distributed sensor and control systems with central processing. The focus is on direct timing estimation for scenarios where clock synchronization is not feasible…

Systems and Control · Computer Science 2013-09-10 John-Olof Nilsson , Peter Händel

We present a collection of different types of observation systems that work as differentiators. These observer-based differentiators can produce estimates for derivatives of a given signal, even though the given signal is prone to noise.

Systems and Control · Electrical Eng. & Systems 2025-10-24 Van Huynh , Hieu Trinh , Riley Bain

We propose a deep unfolding-based approach for stabilization of time-delay linear systems. Deep unfolding is an emerging framework for design and improvement of iterative algorithms and attracting significant attentions in signal…

Optimization and Control · Mathematics 2022-05-20 Masaki Ogura , Koki Kobayashi , Kenji Sugimoto

A novel adaptive identifier is developed for nonlinear time-delay systems composed of linear, Lipschitz and non-Lipschitz components. To begin with, an identifier is designed for uncertain systems with a priori known delay values, and then…

Systems and Control · Electrical Eng. & Systems 2020-05-06 Igor Furtat , Yury Orlov

A single dynamical system with time-delayed feedback can emulate networks. This property of delay systems made them extremely useful tools for Machine Learning applications. Here we describe several possible setups, which allow emulating…

Dynamical Systems · Mathematics 2021-06-30 Florian Stelzer , Serhiy Yanchuk

In this paper we summarize the existing principles for building unconventional computing devices that involve delayed signals for encoding solutions to NP-complete problems. We are interested in the following aspects: the properties of the…

Emerging Technologies · Computer Science 2015-09-10 Mihai Oltean , Oana Muntean

In this work we study local oscillations in delay differential equations with a frequency domain methodology. The main result is a bifurcation equation from which the existence and expressions of local periodic solutions can be determined.…

Dynamical Systems · Mathematics 2017-01-26 Andrea Bel , Walter Reartes , Ana Torresi

In this paper we study the identification of a time-varying linear system from its response to a known input signal. More specifically, we consider systems whose response to the input signal is given by a weighted superposition of delayed…

Information Theory · Computer Science 2021-01-11 Reinhard Heckel , Veniamin I. Morgenshtern , Mahdi Soltanolkotabi

Guided missile accuracy and precision is negatively impacted by seeker delay, more specifically by the delay introduced by a mechanical seeker gimbal and the computational time taken to process the raw data. To meet the demands and…

Systems and Control · Electrical Eng. & Systems 2024-11-14 W. K. Smithson , Xinhua Wang

Distributed multiple-input multiple-output (D-MIMO) is a promising technology for simultaneous communication and positioning. However, phase synchronization between multiple access points in D-MIMO is challenging and methods that function…

Optimization in machine learning, both theoretical and applied, is presently dominated by first-order gradient methods such as stochastic gradient descent. Second-order optimization methods, that involve second derivatives and/or second…

Machine Learning · Computer Science 2021-03-08 Rohan Anil , Vineet Gupta , Tomer Koren , Kevin Regan , Yoram Singer