# Papers

## 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. - 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*, 2017. - 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*, 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, 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, “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.