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.
  3. 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.
  4. 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

  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, 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.
  2. 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.