75 citations · 156 across the 12 of their papers we have counts for
13 papers
Preconditioner Design via the Bregman Divergence
Andreas Bock, Martin S. Andersen
We study a preconditioner for a Hermitian positive definite linear system, which is obtained as the solution of a matrix nearness problem based on the Bregman log determinant diver…
Sparse Bayesian Inference with Regularized Gaussian Distributions
Jasper Marijn Everink, Yiqiu Dong, Martin Skovgaard Andersen
Regularization is a common tool in variational inverse problems to impose assumptions on the parameters of the problem. One such assumption is sparsity, which is commonly promoted…
Bayesian Inference with Projected Densities
Jasper Marijn Everink, Yiqiu Dong, Martin Skovgaard Andersen
Constraints are a natural choice for prior information in Bayesian inference. In various applications, the parameters of interest lie on the boundary of the constraint set. In this…
A Convex Reconstruction Model for X-ray Tomographic Imaging with Uncertain Flat-fields
Hari Om Aggrawal, Martin Skovgaard Andersen, Sean Rose +1
Classical methods for X-ray computed tomography are based on the assumption that the X-ray source intensity is known, but in practice, the intensity is measured and hence uncertain…
Distributed Semidefinite Programming with Application to Large-scale System Analysis
Sina Khoshfetrat Pakazad, Anders Hansson, Martin S. Andersen +1
Distributed algorithms for solving coupled semidefinite programs (SDPs) commonly require many iterations to converge. They also put high computational demand on the computational a…
Tomographic Image Reconstruction using Training images
Sara Soltani, Martin S. Andersen, Per Christian Hansen
We describe and examine an algorithm for tomographic image reconstruction where prior knowledge about the solution is available in the form of training images. We first construct a…
Robust Stability Analysis of Sparsely Interconnected Uncertain Systems
Martin S. Andersen, Sina Khoshfetrat Pakazad, Anders Hansson +1
In this paper, we consider robust stability analysis of large-scale sparsely interconnected uncertain systems. By modeling the interconnections among the subsystems with integral q…
Reduced-Complexity Semidefinite Relaxations of Optimal Power Flow Problems
Martin S. Andersen, Anders Hansson, Lieven Vandenberghe
We propose a new method for generating semidefinite relaxations of optimal power flow problems. The method is based on chordal conversion techniques: by dropping some equality cons…
Generalized Row-Action Methods for Tomographic Imaging
Martin S. Andersen, Per Christian Hansen
Row-action methods play an important role in tomographic image reconstruction. Many such methods can be viewed as incremental gradient methods for minimizing a sum of a large numbe…
Distributed Solutions for Loosely Coupled Feasibility Problems Using Proximal Splitting Methods
Sina Khoshfetrat Pakazad, Martin S. Andersen, Anders Hansson
In this paper, we consider convex feasibility problems where the underlying sets are loosely coupled, and we propose several algorithms to solve such problems in a distributed mann…
Decomposition in conic optimization with partially separable structure
Yifan Sun, Martin S. Andersen, Lieven Vandenberghe
Decomposition techniques for linear programming are difficult to extend to conic optimization problems with general non-polyhedral convex cones because the conic inequalities intro…
Distributed Robust Stability Analysis of Interconnected Uncertain Systems
Martin S. Andersen, Anders Hansson, Sina Khoshfetrat Pakazad +1
This paper considers robust stability analysis of a large network of interconnected uncertain systems. To avoid analyzing the entire network as a single large, lumped system, we mo…
Logarithmic barriers for sparse matrix cones
Martin S. Andersen, Joachim Dahl, Lieven Vandenberghe
Algorithms are presented for evaluating gradients and Hessians of logarithmic barrier functions for two types of convex cones: the cone of positive semidefinite matrices with a giv…