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Found 6 papers, 4 papers with code
Numerical Solution of Stiff ODEs with Physics-Informed RPNNs
We propose a numerical method based on physics-informed Random Projection Neural Networks for the solution of Initial Value Problems (IVPs) of Ordinary Differential Equations (ODEs) with a focus on stiff problems.
Sparse Approximations with Interior Point Methods
Large-scale optimization problems that seek sparse solutions have become ubiquitous.
Portfolio Optimization
Optimization and Control
Numerical Analysis
Numerical Analysis
65K05, 90C51, 90C25, 65F10, 65F08, 90C90
Using gradient directions to get global convergence of Newton-type methods
The renewed interest in Steepest Descent (SD) methods following the work of Barzilai and Borwein [IMA Journal of Numerical Analysis, 8 (1988)] has driven us to consider a globalization strategy based on SD, which is applicable to any line-search method.
Optimization and Control Numerical Analysis Numerical Analysis 65K05, 90C30, 49M15 G.1.6
A subspace-accelerated split Bregman method for sparse data recovery with joint l1-type regularizers
We propose a subspace-accelerated Bregman method for the linearly constrained minimization of functions of the form $f(\mathbf{u})+\tau_1 \|\mathbf{u}\|_1 + \tau_2 \|D\,\mathbf{u}\|_1$, where $f$ is a smooth convex function and $D$ represents a linear operator, e. g. a finite difference operator, as in anisotropic Total Variation and fused-lasso regularizations.
Optimization and Control Numerical Analysis Numerical Analysis 65K05, 90C25 G.1.6
Constraint-Preconditioned Krylov Solvers for Regularized Saddle-Point Systems
We consider the iterative solution of regularized saddle-point systems.
Numerical Analysis Numerical Analysis 65F08, 65F10, 65F50, 90C20
ACQUIRE: an inexact iteratively reweighted norm approach for TV-based Poisson image restoration
We propose a method, called ACQUIRE, for the solution of constrained optimization problems modeling the restoration of images corrupted by Poisson noise.
Optimization and Control 90C25, 65K05, 94A08
