# Publications

## 2024

- A. Bock and M. S. Andersen, “Preconditioner Design via the Bregman Divergence.” 2024.
To appear in SIAM Journal on Matrix Analysis and Applications.
- L. Chen, F. H. Pedersen, and M. S. Andersen, “Matrix Nearness Problems with Off-Block-Diagonal Rank
Constraints,” 2024.
Submitted to Linear Algebra and Its Applications.
- J. V. G. da Mata, A. Hansson, and M. S. Andersen, “Direct System Identification of Dynamical Networks with Partial Measurements: a Maximum Likelihood Approach,” 2024.
To be presented at ECC 2024.

## 2023

- J. M. Everink, Y. Dong, and M. S. Andersen, “Bayesian Inference with Projected Densities,”
*SIAM Journal on Uncertainty Quantification*, 2023. - J. M. Everink, Y. Dong, and M. S. Andersen, “Sparse Bayesian Inference with Regularized Gaussian Distributions,”
*Inverse Problems*, 2023. - F. H. Pedersen, J. S. Jørgensen, and M. S. Andersen, “A Bayesian Approach to CT Reconstruction with Uncertain Geometry,”
*Applied Mathematics in Science and Engineering*, 2023. - K. O. Bangsgaard, G. Burca, E. Ametova, M. S. Andersen, and J. S. Jørgensen, “Low-rank flat-field correction for artifact reduction in spectral computed tomography,”
*Applied Mathematics in Science and Engineering*, 2023. - X. Jiang, Y. Sun, M. S. Andersen, and L. Vandenberghe, “Minimum-rank positive semidefinite matrix completion with chordal patterns and applications to semidefinite relaxations,”
*Applied Set-Valued Analysis and Optimization*, vol. 5, no. 2, Aug. 2023. - J. V. G. da Mata and M. S. Andersen, “AdaSub: Stochastic Optimization Using Second-Order Information in Low-Dimensional Subspaces,” in
*2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)*, 2023. - J. V. G. da Mata and M. S. Andersen, “Link Prediction on Graphs Using NLP Embedding,” in
*2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)*, 2023. - L. Chen, T. Chen, U. Detha, and M. S. Andersen, “Towards Scalable Kernel-Based Regularized System Identification,” in
*2023 62nd IEEE Conference on Decision and Control (CDC)*, 2023. - Z. Shen, Y. Xu, M. S. Andersen, and T. Chen, “An Efficient Implementation for Kernel-based Regularized System Identification with Periodic Input Signals,” in
*2023 62nd IEEE Conference on Decision and Control (CDC)*, 2023. - A. Bock and M. S. Andersen, “A New Matrix Truncation Method for Improving Approximate Factorisation Preconditioners,” 2023.
Submitted for publication.

## 2021

- T. Chen and M. Andersen, “On Semiseparable Kernels and Efficient Implementation for Regularized
System Identification and Function Estimation,”
*Automatica*, vol. 132, 2021. - A. Perelli and M. S. Andersen, “Regularization by Denoising Sub-sampled Newton Method for Spectral CT Multi-Material Decomposition,”
*Philosophical Transactions of the Royal Society A*, vol. 379, no. 2200, 2021. - K. O. Bangsgaard and M. S. Andersen, “A statistical reconstruction model for absorption CT with source uncertainty,”
*Inverse Problems*, vol. 37, no. 8, Jul. 2021.

## 2020

- A. Eltved, J. Dahl, and M. S. Andersen, “On the Robustness and Scalability of Semidefinite Relaxation for Optimal Power Flow Problems,”
*Optimization and Engineering*, vol. 21, no. 2, pp. 375–392, Mar. 2020. - M. S. Andersen and T. Chen, “Smoothing Splines and Rank Structured Matrices: Revisiting the Spline Kernel,”
*SIAM Journal on Matrix Analysis and Applications*, vol. 41, no. 2, pp. 389–412, 2020. - T. Chen and M. S. Andersen, “On Semiseparable Kernels and Efficient Computation of Regularized System Identification and Function Estimation,”
*IFAC-PapersOnLine*, vol. 53, no. 2, pp. 462–467, 2020. - H. O. Aggrawal, M. S. Andersen, and J. Modersitzki, “An Image Registration Framework for Discontinuous Mappings Along Cracks,” in
*Biomedical Image Registration*, 2020, pp. 163–173.

## 2019

- T. Ramos, B. E. Grønager, M. S. Andersen, and J. W. Andreasen, “Direct three-dimensional tomographic reconstruction and phase retrieval of far-field coherent diffraction patterns,”
*Physical Review A*, vol. 99, no. 2, Feb. 2019. - Y. Hu, J. Nagy, J. Zhang, and M. S. Andersen, “Nonlinear optimization for mixed attenuation polyenergetic image reconstruction,”
*Inverse Problems*, no. 6, Jun. 2019. - Y. Hu, M. S. Andersen, and J. G. Nagy, “Spectral Computed Tomography with Linearization and Preconditioning,”
*SIAM Journal on Scientific Computing*, vol. 41, no. 5, pp. S370–S389, Jan. 2019.

## 2018

- H. O. Aggrawal, M. S. Andersen, S. Rose, and E. Y. Sidky, “A Convex Reconstruction Model for X-ray Tomographic Imaging with Uncertain Flat-fields,”
*IEEE Transactions on Computational Imaging*, vol. 4, no. 1, pp. 17–31, Mar. 2018. - S. K. Pakazad, A. Hansson, M. S. Andersen, and A. Rantzer, “Distributed Semidefinite Programming with Application to Large-scale System Analysis,”
*IEEE Transactions on Automatic Control*, vol. 63, no. 4, pp. 1045–1058, Apr. 2018. - D. Kazantsev, J. Jørgensen, M. Andersen, W. Lionheart, P. Lee, and P. Withers, “Joint image reconstruction method with correlative multi-channel prior for X-ray spectral computed tomography,”
*Inverse Problems*, Apr. 2018. - A. Eltved, M. S. Andersen, and O. Borries, “Improved shaping of reflector antennas using a new minimax initialization strategy,” in
*2018 International Applied Computational Electromagnetics Society Symposium (ACES)*, 2018. - A. Eltved, O. Borries, and M. S. Andersen, “Reflector Antenna Optimization using One-Sided Least-Squares,” in
*12th European Conference on Antennas and Propagation (EuCAP 2018)*, 2018. - S. Hong, B. Mu, F. Yin, M. S. Andersen, and T. Chen, “Multiple Kernel Based Regularized System Identification with SURE Hyper-parameter Estimator,” in
*19th IFAC World Congress*, 2018, vol. 51, no. 15, pp. 13–18. - T. Chen, M. S. Andersen, B. Mu, F. Yin, L. Ljung, and S. J. Qin, “Regularized LTI System Identification with Multiple Regularization Matrix,” in
*19th IFAC World Congress*, 2018, vol. 51, no. 15, pp. 180–185.

## 2017

- S. Soltani, M. S. Andersen, and P. C. Hansen, “Tomographic image reconstruction using training images,”
*Journal of Computational and Applied Mathematics*, vol. 313, pp. 243–258, Mar. 2017. - F. Sciacchitano, Y. Dong, and M. S. Andersen, “Total Variation Based Parameter-Free Model for Impulse Noise Removal,”
*Numerical Mathematics: Theory, Methods and Applications*, vol. 10, no. 1, pp. 186–204, 2017.

## 2016

- S. K. Pakazad, A. Hansson, M. S. Andersen, and I. Nielsen, “Distributed primal–dual interior-point methods for solving tree-structured coupled convex problems using message-passing,”
*Optimization Methods and Software*, vol. 32, no. 3, pp. 401–435, Aug. 2016. - J. Li, M. S. Andersen, and L. Vandenberghe, “Inexact proximal Newton methods for self-concordant functions,”
*Mathematical Methods of Operations Research*, vol. 85, no. 1, pp. 19–41, Nov. 2016. - O. Borries, S. B. Sørensen, E. Jørgensen, M. Zhou, M. S. Andersen, and L. E. Sokoler, “Large-scale optimization of contoured beam reflectors and reflectarrays,” in
*2016 IEEE International Symposium on Antennas and Propagation (APSURSI)*, 2016.

## 2015

- L. Vandenberghe and M. S. Andersen, “Chordal Graphs and Semidefinite Optimization,”
*FNT in Optimization*, vol. 1, no. 4, pp. 241–433, 2015. - S. Rose, M. S. Andersen, E. Y. Sidky, and X. Pan, “Noise properties of CT images reconstructed by use of constrained total-variation, data-discrepancy minimization,”
*Medical Physics*, vol. 42, no. 5, pp. 2690–2698, 2015. - O. Lylloff, E. F. Grande, F. Agerkvist, J. Hald, E. T. Roig, and M. S. Andersen, “Improving the efficiency of deconvolution algorithms for sound source localization,”
*The Journal of the Acoustical Society of America*, vol. 138, no. 1, pp. 172–180, 2015.

## 2014

- M. S. Andersen, A. Hansson, and L. Vandenberghe, “Reduced-Complexity Semidefinite Relaxations of Optimal Power Flow Problems,”
*IEEE Transactions on Power Systems*, vol. 29, no. 4, pp. 1855–1863, Jun. 2014. - M. S. Andersen, S. K. Pakazad, A. Hansson, and A. Rantzer, “Robust Stability Analysis of Sparsely Interconnected Uncertain Systems,”
*IEEE Transactions on Automatic Control*, vol. 59, no. 8, pp. 2151–2156, Aug. 2014. - M. S. Andersen and P. C. Hansen, “Generalized Row-Action Methods for Tomographic Imaging,”
*Numerical Algorithms*, vol. 67, no. 1, pp. 121–144, Sep. 2014. - T. Chen, M. S. Andersen, L. Ljung, A. Chiuso, and G. Pillonetto, “System identification via sparse multiple kernel-based regularization using sequential convex optimization techniques,”
*IEEE Transactions on Automatic Control*, vol. 59, no. 11, pp. 2933–2945, Nov. 2014. - S. K. Pakazad, M. S. Andersen, and A. Hansson, “Distributed Solutions for Loosely Coupled Feasibility Problems Using Proximal Splitting Methods,”
*Optimization Methods and Software*, 2014. - Y. Sun, M. S. Andersen, and L. Vandenberghe, “Decomposition in conic optimization with partially separable structure,”
*SIAM Journal on Optimization*, vol. 24, no. 3, pp. 873–897, 2014. - S. K. Pakazad, A. Hansson, M. S. Andersen, and A. Rantzer, “Distributed Robustness Analysis of Interconnected Uncertain Systems Using Chordal Decomposition,” in
*Proc. of the 19th IFAC World Congress*, 2014. - S. K. Pakazad, A. Hansson, and M. S. Andersen, “Distributed Interior-point Method for Loosely Coupled Problems,” in
*Proc. of the 19th IFAC World Congress*, 2014. - S. Rose, E. Y. Sidky, X. Pan, and M. S. Andersen, “Application of incremental algorithms to CT image reconstruction for sparse-view, noisy data,” in
*Proc. of the 3rd International Conference on Image Formation in X-Ray Computed Tomography*, 2014, pp. 351–354. - S. Rose, M. S. Andersen, E. Y. Sidky, and X. Pan, “An efficient ordered subsets CT image reconstruction algorithm for sparse-view, noisy data,” in
*IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)*, 2014. - L. E. Sokoler, G. Frison, M. S. Andersen, and J. B. Jørgensen, “Input-constrained model predictive control via the alternating direction method of multipliers,” in
*Proc. of the 2014 European Control Conference*, 2014, pp. 115–120. - T. Chen, M. S. Andersen, A. Chiuso, G. Pillonetto, and L. Ljung, “Anomaly detection in homogenous populations: A sparse multiple kernel-based regularization method,” in
*Proc. of the 53rd IEEE Conference on Decision and Control*, 2014, pp. 265–270.

## 2013

- M. S. Andersen, J. Dahl, and L. Vandenberghe, “Logarithmic barriers for sparse matrix cones,”
*Optimization Methods and Software*, vol. 28, no. 3, pp. 396–423, 2013.

## 2012

- T. Chen, L. Ljung, M. Andersen, A. Chiuso, F. Carli, and G. Pillonetto, “Sparse multiple kernels for impulse response estimation with majorization minimization algorithms,” in
*Proc. of the 51st IEEE Annual Conference on Decision and Control*, 2012, pp. 1500–1505. - C. Lyzell, M. Andersen, and M. Enqvist, “A convex relaxation of a dimension reduction problem using the nuclear norm,” in
*Proc. of the 51st IEEE Annual Conference on Decision and Control*, 2012, pp. 2852–2857. - M. S. Andersen, A. Hansson, S. K. Pakazad, and A. Rantzer, “Distributed robust stability analysis of interconnected uncertain systems,” in
*Proc. of the 51st IEEE Annual Conference on Decision and Control*, 2012, pp. 1548–1553.

## 2011

- M. S. Andersen, “Chordal Sparsity in Interior-Point Methods for Conic Optimization,” PhD thesis, University of California, Los Angeles, 2011.

## 2010

- M. S. Andersen, J. Dahl, and L. Vandenberghe, “Implementation of nonsymmetric interior-point methods for linear optimization over sparse matrix cones,”
*Mathematical Programming Computation*, vol. 2, no. 3-4, pp. 167–201, Dec. 2010. - M. S. Andersen, L. Vandenberghe, and J. Dahl, “Linear matrix inequalities with chordal sparsity patterns and applications to robust quadratic optimization,” in
*Proc. of the IEEE International Symposium on Computer-Aided Control System Design*, 2010, pp. 7–12. - M. S. Andersen and L. Vandenberghe, “Support vector machine training using matrix completion techniques,” Electrical Engineering Department, University of California, Los Angeles, Mar-2010.
Unpublished report.

## Books and book chapters

- A. Hansson and M. S. Andersen,
*Optimization for Learning and Control*. Wiley, 2023. - M. S. Andersen, J. Dahl, Z. Liu, and L. Vandenberghe, “Interior-point methods for large-scale cone programming,” in
*Optimization for Machine Learning*, S. Sra, S. Nowozin, and S. J. Wright, Eds. MIT Press, 2011, pp. 55–83. - M. S. Andersen, “Optimization Method for Tomography,” in
*Computed Tomography: Algorithms, Insight, and Just Enough Theory*, P. C. Hansen, J. S. Jørgensen, and W. R. B. Lionheart, Eds. Society for Industrial and Applied Mathematics (SIAM), 2021, pp. 275–315.