19 papers
Hyperspectral Unmixing Hierarchies
Joseph L. Garrett, P. S. Vishnu, Pauliina Salmi +4
Unmixing reveals the spatial distribution and spectral details of different constituents, called endmembers, in a hyperspectral image. Because unmixing has limited ground truth req…
Complexity of a linearized augmented Lagrangian method for nonconvex minimization with nonlinear equality constraints
Lahcen El Bourkhissi, Ion Necoara
In this paper, we consider a nonconvex optimization problem with nonlinear equality constraints. We assume that both, the objective function and the functional constraints are loca…
An accelerated randomized Bregman-Kaczmarz method for strongly convex linearly constraint optimization
Lionel Tondji, Dirk A. Lorenz, Ion Necoara
In this paper, we propose a randomized accelerated method for the minimization of a strongly convex function under linear constraints. The method is of Kaczmarz-type, i.e. it only…
Modified projected Gauss-Newton method for constrained nonlinear least-squares: application to power flow analysis
Yassine Nabou, Lucian Toma, Ion Necoara
In this paper, we consider a modified projected Gauss-Newton method for solving constrained nonlinear least-squares problems. We assume that the functional constraints are smooth a…
Coordinate projected gradient descent minimization and its application to orthogonal nonnegative matrix factorization
Flavia Chorobura, Daniela Lupu, Ion Necoara
In this paper we consider large-scale composite nonconvex optimization problems having the objective function formed as a sum of three terms, first has block coordinate-wise Lipsch…
A stochastic perturbed augmented Lagrangian method for smooth convex constrained minimization
Nitesh Kumar Singh, Ion Necoara
This paper considers smooth convex optimization problems with many functional constraints. To solve this general class of problems we propose a new stochastic perturbed augmented L…