2024

  1. L. Chen, F. H. Pedersen, and M. S. Andersen, “Matrix Nearness Problems with Off-Block-Diagonal Rank Constraints,” Linear Algebra and Its Applications, 2024.
  2. A. Bock and M. S. Andersen, “Preconditioner Design via the Bregman Divergence,” SIAM Journal on Matrix Analysis and Applications, vol. 45, no. 2, pp. 1148–1182, 2024.
  3. R. Laumont, Y. Dong, and M. S. Andersen, “Sampling strategies in Bayesian inversion: A study of RTO and Langevin methods,” Inverse Problems and Imaging, 2024.
  4. J. V. G. Da Mata, A. Hansson, and M. S. Andersen, “Direct System Identification of Dynamical Networks with Partial Measurements: A Maximum Likelihood Approach,” in 2024 European Control Conference (ECC), 2024.
  5. M. H. Gæde, M. S. Andersen, A. Limkilde, O. Borries, and J. S. Hesthaven, “A Fast Direct Solver for Higher Order Discretizations of Integral Equations,” in 2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI), 2024, pp. 497–498.
  6. J. Everink, Y. Dong, and M. S. Andersen, “The Geometry and Well-Posedness of Sparse Regularized Linear Regression,” 2024.

2023

  1. 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.
  2. 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.
  3. 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.
  4. J. M. Everink, Y. Dong, and M. S. Andersen, “Bayesian Inference with Projected Densities,” SIAM Journal on Uncertainty Quantification, 2023.
  5. J. M. Everink, Y. Dong, and M. S. Andersen, “Sparse Bayesian Inference with Regularized Gaussian Distributions,” Inverse Problems, 2023.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. A. Bock and M. S. Andersen, “A New Matrix Truncation Method for Improving Approximate Factorisation Preconditioners,” 2023.

2021

  1. T. Chen and M. Andersen, “On Semiseparable Kernels and Efficient Implementation for Regularized System Identification and Function Estimation,” Automatica, vol. 132, 2021.
  2. 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.
  3. 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

  1. 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.
  2. 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.
  3. 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.
  4. 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

  1. 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.
  2. Y. Hu, J. Nagy, J. Zhang, and M. S. Andersen, “Nonlinear optimization for mixed attenuation polyenergetic image reconstruction,” Inverse Problems, no. 6, Jun. 2019.
  3. 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

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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

  1. 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.
  2. 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

  1. 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.
  2. 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.
  3. 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

  1. L. Vandenberghe and M. S. Andersen, “Chordal Graphs and Semidefinite Optimization,” FNT in Optimization, vol. 1, no. 4, pp. 241–433, 2015.
  2. 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.
  3. 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

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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

  1. 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

  1. 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.
  2. 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.
  3. 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

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

2010

  1. 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.
  2. 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.
  3. 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

  1. A. Hansson and M. S. Andersen, Optimization for Learning and Control. Wiley, 2023.
  2. 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.
  3. 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.