Publications and Reports

Selected Talks

• Polar Duality in three liftings (slides), McGill University, Montreal. Nov 28, 2017

• Algorithms for sparse optimiziation (slides), Google Research, Mountain View. May 19, 2015

• Robust inversion and randomized sampling (slides), International Symposium on Mathematical Programming (ISMP), Berlin. August 19—24, 2012

Papers and Reports

• Fast training for large-scale one-versus-all linear classifiers using tree-structured initialization, H. Fang, M. Cheng, C.-J. Hsieh, and M. P. Friedlander. SIAM International Conference on Data Mining (SDM19), 2019

• Implementing a smooth exact penalty function for equality-constrained nonlinear optimization, R. Estrin, M. P. Friedlander, D. Orban, and M. A. Saunders. January 2019

• Smooth structured prediction using quantum and classical Gibbs samplers, B. Sepehry, E. Iranmanesh, M. P. Friedlander, and P. Ronagh. September 2018

• Polar convolution. M. P. Friedlander, I. Macêdo, and T. K. Pong. August 2018

• A perturbation view of level-set methods for convex optimization. R. Estrin and M. P. Friedlander. July 2018

• Foundations of gauge and perspective duality. A. Y. Aravkin, J. V. Burke, D. Drusvyatskiy, M. P. Friedlander, and K. MacPhee. SIAM J. Optim. 28(3):2406-2434, 2018

• Efficient evaluation of scaled proximal operators. M. P. Friedlander and G. Goh. Electronic Trans. Numerical Analysis, 46:1-22, 2017

• Satisfying real-world goals with dataset constraints. G. Goh, A. Cotter, M. Gupta, M. P. Friedlander. NIPS 2016. Barcelona, Spain, December 2016

• Social Resistance. M. P. Friedlander, N. Krislock, T. K. Pong. IEEE Computing in Science and Engineering, March 2016; and Computing Edge, August 2016

• Level-set methods for convex optimization. A. Y. Aravkin, J. V. Burke, D. Drusvyatskiy, M. P. Friedlander, S. Roy. February 2016. To appear in Mathematical Programming

• Low-rank spectral optimization via gauge duality. M. P. Friedlander and I. Macêdo. SIAM J. on Scientific Computing, 38(3):A1616-A1638, 2016

• Coordinate descent converges faster with the gauss-southwell rule than random selection. J. Nutini, M. Schmidt, I. H. Laradji, M. P. Friedlander, H. Koepke. Proc 32nd Inter. Conf on Machine Learning (ICML-15), Lille, France, July 2015

• Gauge optimization and duality. M. P. Friedlander, I. Macêdo, and T. K. Pong. SIAM J. on Optimization, 24(4):1999-2022, 2014

• Fast dual variational inference for non-conjugate latent gaussian models. M. E. Khan, A. Y. Aravkin, M. P. Friedlander, and M. Seeger. Proc. 30th Inter. Conf. on Machine Learning (ICML-13), Atlanta. May 2013

• Variational properties of value functions. A. Aravkin, J. V. Burke, and M. P. Friedlander. SIAM J. on Optimization, 23(3):1689-1717, 2013

• Robust inversion via semistochastic dimensionality reduction. A. Aravkin, M. P. Friedlander, and T. van Leeuwen. IEEE Conf. Acoustics, Speech, and Signal Proc., 2012

• Robust inversion, dimensionality reduction, and randomized sampling. A. Aravkin, M. P. Friedlander, F. Herrmann, and T. van Leeuwen. Mathematical Programming, 134(1):101-125, 2012

• Fighting the curse of dimensionality: compressive sensing in exploration seismology. F. J. Herrmann, M. P. Friedlander, and O. Yilmaz. IEEE Signal Processing Magazine, 29(3):88—100, 2012

• Hybrid deterministic-stochastic methods for data fitting. M. P. Friedlander and M. Schmidt. SIAM J. on Scientific Computing, 34(3):A1380–A1405, 2012

• A primal-dual regularized interior-point method for convex quadratic programs. M. P. Friedlander and D. Orban. Mathematical Programming Computation, 4(1):71-107, 2012

• Sparse optimization with least-squares constraints. E. van den Berg and M. P. Friedlander, SIAM J. on Optimization, 21(4):1201–1229, 2011

• Recovering compressively sampled signals using partial support information. M. P. Friedlander, H. Mansour, R. Saab, O. Yilmaz. IEEE Trans. on Information Theory, 58(2):1122-1134, 2011

• Theoretical and empirical results for recovery from multiple measurements. E. van den Berg and M. P. Friedlander, IEEE Trans. on Information Theory, 56(5):2516-2527, 2010

• Optimizing Costly Functions with Simple Constraints: A Limited-Memory Projected Quasi-Newton Algorithm. M. Schmidt, E. van den Berg, M. P. Friedlander, and K. Murphy, Proc. of the 12th Inter. Conf. on Artificial Intelligence and Statistics (AISTATS) 2009, J. Machine Learning Research, W&CP 5, April 2009 (received “Best Paper” award)

• Sparco: a testing framework for sparse reconstruction. E. van den Berg, M. P. Friedlander, G. Hennenfent, F. Herrmann, R. Saab, and O. Yilmaz, ACM Trans. on Mathematical Software, 35(4):1-16, February 2009

• Probing the Pareto frontier for basis pursuit solutions. E. van den Berg and M. P. Friedlander, SIAM J. on Scientific Computing, 31(2):890-912, November 2008

• Computing nonnegative tensor factorizations. M. P. Friedlander and K. Hatz, Optimization Methods and Software, 23(4):631-647, August 2008

• New insights into one-norm solvers from the Pareto curve. G. Hennenfent, E. van den Berg, M. P. Friedlander, and F. Herrmann, Geophysics, 73(4):A23-A26, July 2008

• Global and finite termination of a two-phase augmented Lagrangian filter method for general quadratic programs. M. P. Friedlander and S. Leyffer, SIAM J. on Scientific Computing, 30(4):1706-1726, April 2008

• Discussion: The Dantzig selector: Statistical estimation when p is much larger than n. M. P. Friedlander and M. A. Saunders, Annals of Statistics, 35(6):2385-2391, December 2007

• Exact regularization of convex programs. M. P. Friedlander and P. Tseng, SIAM J. on Optimization, 18(4):1326-1350, November 2007

• A filter active-set trust-region method. M. P. Friedlander, N. I. M. Gould, S. Leyffer, and T. S. Munson, Preprint ANL/MCS-P1456-0907, Argonne National Laboratory, September 2007

• In pursuit of a root. E. van den Berg and M. P. Friedlander, Tech. Rep. TR-2007-19, Dept of Computer Science, Univ of British Columbia, June 2007

• Diffuse optical fluorescence tomography using time-resolved data acquired in transmission. F. Leblond, S. Fortier, and M. P. Friedlander, In Fred S. Azar, editor, Multimodal Biomedical Imaging II, vol. 6431. Proceedings of the International Society of Optimal Imaging, February 2007

• On minimizing distortion and relative entropy. M. P. Friedlander and M. R. Gupta, IEEE Transactions on Information Theory, 52(1):238-245, January 2006

• Exact regularization of linear programs. M. P. Friedlander, Tech. Rep. TR-2005-31, Dept of Computer Science, Univ of British Columbia, December 2005

• A two-sided relaxation scheme for mathematical programs with equilibrium constraints. A.-V. DeMiguel, M. P. Friedlander, F. J. Nogales, and S. Scholtes, SIAM J. on Optimization, 16(1):587-609, 2005

• A globally convergent linearly constrained Lagrangian methods for nonlinear optimization. M. P. Friedlander and M. A. Saunders, SIAM J. on Optimization 15(3), 863-897, 2005

• Maximum entropy classification applied to speech. M. R. Gupta, M. P. Friedlander, and R. M. Gray, In Conference Record of the Thirty-Fourth Asilomar Conference on Signals, Systems and Computers, vol. 2, 1480-1483, October 2000