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Trajectory optimization of low-thrust perturbed orbit rendezvous is a crucial technology for space missions in low Earth orbits, which is difficult to solve due to its initial value sensitivity, especially when the transfer trajectory has…

Instrumentation and Methods for Astrophysics · Physics 2023-05-03 An-yi Huang , Heng-nian Li

Minimum achievable complexity (MAC) for a maximum likelihood (ML) performance-achieving detection algorithm is derived. Using the derived MAC, we prove that the conventional sphere decoding (SD) algorithms suffer from an inherent weakness…

Information Theory · Computer Science 2021-09-21 Mohammad Neinavaie , Mostafa Derakhtian , Sergiy A. Vorobyov

Beginning with a Heisenberg spin Hamiltonian for the manganese ions in the Mn_12 Ac molecule, we find a number of low-energy states of the system. We use these states to solve the time-dependent Schrodinger equation and find the…

Mesoscale and Nanoscale Physics · Physics 2009-10-31 Indranil Rudra , S. Ramasesha , Diptiman Sen

This article presents an approach for modelling hysteresis in piezoelectric materials, that leverages recent advancements in machine learning, particularly in sparse-regression techniques. While sparse regression has previously been used to…

Machine Learning · Computer Science 2023-05-23 Abhishek Chandra , Bram Daniels , Mitrofan Curti , Koen Tiels , Elena A. Lomonova , Daniel M. Tartakovsky

This paper proposes an improved quasi-Newton penalty decomposition algorithm for the minimization of continuously differentiable functions, possibly nonconvex, over sparse symmetric sets. The method solves a sequence of penalty subproblems…

Optimization and Control · Mathematics 2026-01-21 Ahmad Mousavi , Morteza Kimiaei , Saman Babaie-Kafaki , Vyacheslav Kungurtsev

Optimizing over the set of orthogonal matrices is a central component in problems like sparse-PCA or tensor decomposition. Unfortunately, such optimization is hard since simple operations on orthogonal matrices easily break orthogonality,…

Machine Learning · Computer Science 2013-12-16 Uri Shalit , Gal Chechik

This paper develops a charge-conservative mixed finite element method with optimal convergence rates for the stationary incompressible inductionless MHD equations on three-dimensional curved domains. The discretization employs the…

Numerical Analysis · Mathematics 2026-02-24 Xue Jiang , Lei Li , Lingxiao Li

Gradient methods are frequently used in large scale image deblurring problems since they avoid the onerous computation of the Hessian matrix of the objective function. Second order information is typically sought by a clever choice of the…

Numerical Analysis · Mathematics 2015-11-19 Federica Porta , Marco Prato , Luca Zanni

The breakthrough ideas in the modern proximal splitting methodologies allow us to express the set of all minimizers of a superposition of multiple nonsmooth convex functions as the fixed point set of computable nonexpansive operators. In…

Optimization and Control · Mathematics 2022-07-01 Isao Yamada , Masao Yamagishi

In this work, we consider efficient maximum-likelihood decoding of linear block codes for small-to-moderate block lengths. The presented approach is a branch-and-bound algorithm using the cutting-plane approach of Zhang and Siegel (IEEE…

Information Theory · Computer Science 2014-04-29 Michael Helmling , Eirik Rosnes , Stefan Ruzika , Stefan Scholl

A multiscale (micro-to-macro) analysis is proposed for the prediction of the finite strain behavior of composites with hyperelastic constituents and embedded localized damage. The composites are assumed to possess periodic microstructure…

Materials Science · Physics 2020-10-20 Nathan Perchikov , Jacob Aboudi

In this paper, we design a novel algorithm based on Least-Squares Monte Carlo (LSMC) in order to approximate the solution of discrete time Backward Stochastic Differential Equations (BSDEs). Our algorithm allows massive parallelization of…

Numerical Analysis · Mathematics 2024-08-01 E. Gobet , J. G. López-Salas , P. Turkedjiev , C. Vázquez

The Landau-Lifshitz-Gilbert (LLG) equation, regarded as a gradient flow with manifold constraint, is the fundamental model describing magnetization dynamics in ferromagnetic materials. It is well known that the normalized tangent plane…

Numerical Analysis · Mathematics 2026-02-10 Binghong Li , Xiaoli Li , Cheng Wang , Jiang Yang

We develop a spectral low-mode reduced solver for second-order elliptic boundary value problems with spatially varying diffusion coefficients. The approach projects standard finite difference or finite element discretization onto a global…

Numerical Analysis · Mathematics 2025-12-23 Prosper Torsu

We consider a multiscale strategy addressing the disparate scales in the Landau-Lifschitz equations in micro-magnetism. At the microscopic scale, the dynamics of magnetic moments are driven by a high frequency field. On the macroscopic…

Numerical Analysis · Mathematics 2017-03-16 Doghonay Arjmand , Stefan Engblom , Gunilla Kreiss

The Landau--Lifshitz--Baryakhtar (LLBar) and the Landau--Lifshitz--Bloch (LLBloch) equations are nonlinear vector-valued PDEs which arise in the theory of micromagnetics to describe the dynamics of magnetic spin field in a ferromagnet at…

Numerical Analysis · Mathematics 2025-05-27 Agus L. Soenjaya

Stiffness and hysteretic energy losses are calculated for a magnetically levitating system composed of a type-II superconductor and a permanent magnet when a small vibration is produced in the system. We consider a cylindrically symmetric…

Superconductivity · Physics 2009-11-07 Carles Navau , Alvaro Sanchez

We present a Heterogeneous Multiscale Method for the Landau-Lifshitz equation with a highly oscillatory diffusion coefficient, a simple model for a ferromagnetic composite. A finite element macro scheme is combined with a finite difference…

Numerical Analysis · Mathematics 2021-11-23 Lena Leitenmaier , Murtazo Nazarov

This work introduces a latent space method to calculate the demagnetization reversal process of multigrain permanent magnets. The algorithm consists of two deep learning models based on neural networks. The embedded Stoner-Wohlfarth method…

This paper proposes a homotopy coordinate descent (HCD) method to solve the $l_0$-norm regularized least square ($l_0$-LS) problem for compressed sensing, which combine the homotopy technique with a variant of coordinate descent method.…

Machine Learning · Computer Science 2020-11-16 Zhenzhen Sun , Yuanlong Yu