Results 71 to 80 of about 38,694 (221)
A penalty method for PDE-constrained optimization in inverse problems
, 2015 Many inverse and parameter estimation problems can be written as PDE-constrained optimization problems. The goal, then, is to infer the parameters, typically coefficients of the PDE, from partial measurements of the solutions of the PDE for several right-
Herrmann, Felix J., van Leeuwen, Tristan
core +1 more source
Local Polynomial Regression and Filtering for a Versatile Mesh‐Free PDE Solver
International Journal for Numerical Methods in Fluids, EarlyView.A high‐order, mesh‐free finite difference method for solving differential equations is presented. Both derivative approximation and scheme stabilisation is carried out by parametric or non‐parametric local polynomial regression, making the resulting numerical method accurate, simple and versatile. Numerous numerical benchmark tests are investigated for
Alberto M. Gambaruto
wiley +1 more source
An Augmented Lagrangian Approach for Sparse Principal Component Analysis [PDF]
, 2009 Principal component analysis (PCA) is a widely used technique for data analysis and dimension reduction with numerous applications in science and engineering. However, the standard PCA suffers from the fact that the principal components (PCs) are usually
Lu, Zhaosong, Zhang, Yong
core +2 more sources
Hybridized augmented Lagrangian methods for contact problems
Computer Methods in Applied Mechanics and EngineeringThis paper addresses the problem of friction-free contact between two elastic bodies. We develop an augmented Lagrangian method that provides computational convenience by reformulating the contact problem as a nonlinear variational equality. To achieve this, we propose a Nitsche-based method incorporating a hybrid displacement variable defined on an ...
Erik Burman +2 more
openaire +3 more sources
A First-Order Augmented Lagrangian Method for Compressed Sensing [PDF]
SIAM Journal on Optimization, 2012 We propose a first-order augmented Lagrangian algorithm (FAL) for solving the basis pursuit problem. FAL computes a solution to this problem by inexactly solving a sequence of L1-regularized least squares sub-problems. These sub-problems are solved using an infinite memory proximal gradient algorithm wherein each update reduces to "shrinkage" or ...
Necdet S. Aybat, Garud Iyengar
openaire +2 more sources
Robust Linearization and Eigenvalue Analysis of General Complex Constrained Multibody Systems
International Journal of Mechanical System Dynamics, EarlyView.ABSTRACT The derivation of linearized equations and subsequent eigenvalue analysis is the basis for tasks such as frequency‐domain response analysis, control design, and stability assessment for mechanical systems. However, for general multibody systems with redundant or nonholonomic constraints, practical challenges persist in achieving numerically ...
Zhiwen Xiao, Gexue Ren
wiley +1 more source
Thermo‐Hydrodynamic Processes of Microparticles in Fractures: A CFD–DEM Approach
International Journal for Numerical and Analytical Methods in Geomechanics, EarlyView.ABSTRACT Controlling fracture permeability plays a critical role in subsurface energy applications, particularly in geothermal energy production and enhanced carbon sequestration, where regulating fluid flow is essential. The use of microcapsules has become a promising solution to enhance fracture permeability by providing a controlled sealing ...
Hoai Thanh Nguyen, Pania Newell
wiley +1 more source
On Augmented Lagrangian Decomposition Methods for Multistage Stochastic Programs [PDF]
, 1994 A general decomposition framework for large convex optimization problems based on augmented Lagrangians is described. The approach is then applied to multistage stochastic programming problems in two different ways: by decomposing the problem into ...
Rosa, C.H., Ruszczynski, A.
core
Dual-Augmented Lagrangian Method for Efficient Sparse Reconstruction [PDF]
IEEE Signal Processing Letters, 2009 We propose an efficient algorithm for sparse signal reconstruction problems. The proposed algorithm is an augmented Lagrangian method based on the dual sparse reconstruction problem. It is efficient when the number of unknown variables is much larger than the number of observations because of the dual formulation.
Tomioka, R., Sugiyama, M.
openaire +2 more sources
A review of the FATIMA Yellow Sea field campaign research
Quarterly Journal of the Royal Meteorological Society, EarlyView.The FATIMA marine fog project took place over the Sable Island region of eastern Canada (summer 2022) and the Yellow Sea of the Republic of Korea (ROK, summer 2023). Its goals are to improve marine fog monitoring and forecasting. Instrumented multiple research vessels, aircraft, tethered balloon and autonomous systems, ocean observation sites, and ...
Seok Lee +50 more
wiley +1 more source