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This work begins by establishing a mathematical formalization between different geometrical interpretations of Neural Networks, providing a first contribution. From this starting point, a new interpretation is explored, using the idea of…

Machine Learning · Computer Science 2019-05-20 Daniel Vieira , Joao Paixao

This paper is devoted to studying the first-order variational analysis of non-convex and non-differentiable functions that may not be subdifferentially regular. To achieve this goal, we entirely rely on two concepts of directional…

Optimization and Control · Mathematics 2022-04-22 Ashkan Mohammadi

Based on the needs of convergence proofs of preconditioned proximal point methods, we introduce notions of partial strong submonotonicity and partial (metric) subregularity of set-valued maps. We study relationships between these two…

Optimization and Control · Mathematics 2020-03-02 Tuomo Valkonen

Weak values are typically obtained experimentally by performing weak measurements, which involve weak interactions between the measured system and a probe. However, the determination of weak values does not necessarily require weak…

Physical systems experience nonlinear disturbances which have the potential to disrupt desired behavior. For a particular disturbance, whether or not the system recovers from the disturbance to a desired stable equilibrium point depends on…

Systems and Control · Electrical Eng. & Systems 2025-01-14 Michael W. Fisher

The main theme of the article is the study of discrete systems of material points subjected to constraints not only of a geometric type (holonomic constraints) but also of a kinematic type (nonholonomic constraints). The setting up of the…

Classical Physics · Physics 2023-05-30 Federico Talamucci

This work focuses on angular values of nonautonomous dynamical systems which have been introduced for general random and (non)autonomous dynamical systems in a previous publication [W.-J. Beyn, G. Froyland, and T. H\"uls, SIAM J. Appl. Dyn.…

Dynamical Systems · Mathematics 2023-02-22 Wolf-Jürgen Beyn , Thorsten Hüls

In this paper, the notion of contraction is used to solve the trajectory-tracking problem for a class of mechanical systems. Additionally, we propose a dynamic extension to remove velocity measurements from the controller while rejecting…

Systems and Control · Electrical Eng. & Systems 2023-07-04 N. Javanmardi , P. Borja , M. J. Yazdanpanah , J. M. A. Scherpen

This paper considers the robustness of an uncertain nonlinear system along a finite-horizon trajectory. The uncertain system is modeled as a connection of a nonlinear system and a perturbation. The analysis relies on three ingredients.…

Systems and Control · Electrical Eng. & Systems 2025-08-05 Peter Seiler , Raghu Venkataraman

A new pattern search method for bound constrained optimization is introduced. The proposed algorithm employs the coordinate directions, in a suitable way, with a nonmonotone line search for accepting the new iterate, without using…

Optimization and Control · Mathematics 2018-06-25 Johanna A. Frau , Elvio A. Pilotta

Supervised linear feature extraction can be achieved by fitting a reduced rank multivariate model. This paper studies rank penalized and rank constrained vector generalized linear models. From the perspective of thresholding rules, we build…

Machine Learning · Statistics 2012-05-11 Yiyuan She

With recent advances in sensing and tracking technology, trajectory data is becoming increasingly pervasive and analysis of trajectory data is becoming exceedingly important. A fundamental problem in analyzing trajectory data is that of…

Computational Geometry · Computer Science 2013-03-08 Swaminathan Sankararaman , Pankaj K. Agarwal , Thomas Mølhave , Arnold P. Boedihardjo

We present the Complex Envelope Variable Approximation (CEVA) as the very useful and compact method for the analysis of the essentially nonlinear dynamical systems. It allows us to study both the stationary and non-stationary dynamics even…

Pattern Formation and Solitons · Physics 2020-04-20 Valeri V. Smirnov , Leonid I. Manevitch

In this note, we propose a novel approach for a class of autonomous dynamical systems that allows, given some observations of the solutions, to identify its parameters and reconstruct the state vector. This approach relies on proving the…

Dynamical Systems · Mathematics 2024-08-22 Alicja B Kubik , Alain Rapaport , Benjamin Ivorra , Ángel M Ramos

A block decomposition method is proposed for minimizing a (possibly non-convex) continuously differentiable function subject to one linear equality constraint and simple bounds on the variables. The proposed method iteratively selects a…

Optimization and Control · Mathematics 2019-03-06 Andrea Cristofari

This work considers the nonconvex, nonsmooth problem of minimizing a composite objective of the form $f(g(x))+h(x)$ where the inner mapping $g$ is a smooth finite summation or expectation amenable to variance reduction. In such settings,…

Optimization and Control · Mathematics 2025-10-16 Yue Wu , Benjamin Grimmer

Matrix measures induced by vector norms are widely used in contraction theory of nonlinear dynamical systems. A natural and important robustness question is whether negativity of a matrix measure is preserved under arbitrary nonnegative…

Optimization and Control · Mathematics 2026-05-26 Ron Ofir , Michael Margaliot

Contraction theory is a mathematical framework for studying the convergence, robustness, and modularity properties of dynamical systems and algorithms. In this opinion paper, we provide five main opinions on the virtues of contraction…

Systems and Control · Electrical Eng. & Systems 2025-07-24 Alexander Davydov , Francesco Bullo

The reduction of dynamical systems has a rich history, with many important applications related to stability, control and verification. Reduction of nonlinear systems is typically performed in an exact manner - as is the case with…

Optimization and Control · Mathematics 2007-07-26 Paulo Tabuada , Aaron D. Ames , Agung Julius , George J. Pappas

We propose a new embedding method for a single vector and for a pair of vectors. This embedding method enables: a) efficient classification and regression of functions of single vectors; b) efficient approximation of distance functions; and…

Machine Learning · Computer Science 2016-08-09 Ofir Pele , Yakir Ben-Aliz
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