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This work presents a sum-of-squares (SOS) based framework to perform data-driven stabilization and robust control tasks on discrete-time linear systems where the full-state observations are corrupted by L-infinity bounded input,…

Optimization and Control · Mathematics 2023-03-31 Jared Miller , Tianyu Dai , Mario Sznaier

Solving the three-dimensional (3D) Bratu equation is highly challenging due to the presence of multiple and sharp solutions. Research on this equation began in the late 1990s, but there are no satisfactory results to date. To address this…

Numerical Analysis · Mathematics 2025-07-22 Muhammad Luthfi Shahab , Hadi Susanto , Haralampos Hatzikirou

This paper deals with the state estimation of stochastic models with continuous dynamics. The aim is to incorporate spectral differentiation methods into the solution to the Fokker-Planck equation in grid-based state estimation routine,…

Signal Processing · Electrical Eng. & Systems 2025-01-20 Jakub Matousek , Jindrich Dunik , Marek Brandner

This paper proposes a novel Generalized Non-Standard Finite Difference (GNSFD) scheme for the numerical solution of a class of fractional partial differential equations (FrPDEs). The formulation of the method is grounded in optimization and…

Numerical Analysis · Mathematics 2025-09-17 Devank Mishra , Sheerin Kayenat , Amit K. Verma

The reduction of computational costs in the numerical solution of nonstationary problems is achieved through splitting schemes. In this case, solving a set of less computationally complex problems provides the transition to a new level in…

Numerical Analysis · Mathematics 2022-10-26 Petr N. Vabishchevich

Conformal Prediction (CP) is a distribution-free framework for constructing statistically rigorous prediction sets. While popular variants such as CD-split improve CP's efficiency, they often yield prediction sets composed of multiple…

Machine Learning · Statistics 2025-09-29 Mingyi Zheng , Hongyu Jiang , Yizhou Lu , Jiaye Teng

This paper proposes a novel parallel stochastic gradient descent (SGD) method that is obtained by applying parallel sets of SGD iterations (each set operating on one node using the data residing in it) for finding the direction in each…

Machine Learning · Computer Science 2013-11-05 Dhruv Mahajan , S. Sathiya Keerthi , S. Sundararajan , Leon Bottou

In this paper, we study the performance of a large family of SGD variants in the smooth nonconvex regime. To this end, we propose a generic and flexible assumption capable of accurate modeling of the second moment of the stochastic…

Optimization and Control · Mathematics 2020-06-15 Zhize Li , Peter Richtárik

The Residual Smooting Scheme (RSS) have been introduced in \cite{AverbuchCohenIsraeli} as a backward Euler's method with a simplified implicit part for the solution of parabolic problems. RSS have stability properties comparable to those of…

Numerical Analysis · Mathematics 2015-06-24 Brachet Matthieu , Chehab Jean-Paul

Various classes of stable finite difference schemes can be constructed to obtain a numerical solution. It is important to select among all stable schemes such a scheme that is optimal in terms of certain additional criteria. In this study,…

Numerical Analysis · Computer Science 2010-06-01 Petr N. Vabishchevich

A generalised analytical notion of summation-by-parts (SBP) methods is proposed, extending the concept of SBP operators in the correction procedure via reconstruction (CPR), a framework of high-order methods for conservation laws. For the…

Numerical Analysis · Mathematics 2017-04-26 Hendrik Ranocha , Philipp Öffner , Thomas Sonar

In this work, we present a numerical method that remedies the instabilities of the conventional FDTD approach for solving Maxwell's equations in a space-time dependent magneto-electric medium with direct application to the simulation of the…

Optics · Physics 2015-06-18 Jason Cornelius , Jinjie Liu , Moysey Brio

One of the most common methods to train machine learning algorithms today is the stochastic gradient descent (SGD). In a distributed setting, SGD-based algorithms have been shown to converge theoretically under specific circumstances. A…

Machine Learning · Computer Science 2025-08-22 Soumya Sarkar , Shweta Jain

The word stable is used to describe a situation when mathematical objects that almost satisfy an equation are close to objects satisfying it exactly. We study operator-algebraic forms of stability for unitary representations of groups and…

Operator Algebras · Mathematics 2025-09-22 Mikael de la Salle

Smoothed Dissipative Particle Dynamics (SDPD) is a mesoscopic particle method which allows to select the level of resolution at which a fluid is simulated. The numerical integration of its equations of motion still suffers from the lack of…

Statistical Mechanics · Physics 2017-10-25 Gérôme Faure , Gabriel Stoltz

Kohn-Sham density functional theory (DFT) has long struggled with the accurate description of strongly correlated and open shell systems and improvements have been minor even in the newest hybrid functionals. In this Letter we treat the…

Chemical Physics · Physics 2021-04-01 Danny Gibney , Jan-Niklas Boyn , David A. Mazziotti

Density gradient theory (DGT) allows fast and accurate determination of surface tension and density profile through a phase interface. Several algorithms have been developed to apply this theory in practical calculations. While the…

Chemical Physics · Physics 2016-08-09 Xiaoqun Mu , Florian Frank , Faruk O. Alpak , Walter G. Chapman

Recent advances in symbolic dynamic programming (SDP) combined with the extended algebraic decision diagram (XADD) data structure have provided exact solutions for mixed discrete and continuous (hybrid) MDPs with piecewise linear dynamics…

Artificial Intelligence · Computer Science 2013-09-27 Luis Gustavo Vianna , Scott Sanner , Leliane Nunes de Barros

Symmetries are intrinsic to many combinatorial problems including Boolean Satisfiability (SAT) and Constraint Programming (CP). In SAT, the identification of symmetry breaking predicates (SBPs) is a well-known, often effective, technique…

Artificial Intelligence · Computer Science 2008-12-18 Joao Marques-Silva , Ines Lynce , Vasco Manquinho

We consider large linear and nonlinear fixed point problems, and solution with proximal algorithms. We show that there is a close connection between two seemingly different types of methods from distinct fields: 1) Proximal iterations for…

Numerical Analysis · Computer Science 2019-09-05 Dimitri P. Bertsekas