RECENT PUBLICATIONS
  • A. Beck, M.Teboulle
    Mirror Descent and Nonlinear Projected Subgradient Methods for Convex Optimization
    Operations Research Letters, 31, (2003), 167-175


  • J. Bolte, M. Teboulle
    Barrier operators and associated gradient like dynamical systems for constrained minimization problems
    SIAM J. of Control and Optimization, 42, (2003), 1266-1292


  • A. Auslender, M. Teboulle
    The Log-Quadratic proximal methodology in convex optimization algorithms and variational inequalities
    in "Equilibrium Problems and Variational Methods", Edited by P. Daniel, F. Gianessi and A. Maugeri
    Nonconvex Optimization and its Applications, Vol 68, Kluwer Academic Press, (2003).


  • A. Beck, M. Teboulle
    Convergence rate analysis and error bounds for projection algorithms in convex feasibility problems
    Optimization and Software, 18, (2003), 377-394


  • H. Attouch and M. Teboulle
    A regularized Lotka-Volterra dynamical system as a continuous proximal-like method in optimization
    Journal of Optimization Theory and Applications, 121, ( 2004), 541--570.


  • A. Auslender, M. Teboulle
    Interior gradient and epsilon-subgradient descent methods for constrained convex minimization
    Mathematics of Operations research, 29, (2004), 1-26


  • A. Beck, M. Teboulle
    A conditional gradient method with linear rate of convergence for solving convex linear systems
    Mathematical Methods of Operations Research, 59, (2004), 235-247.


  • A. Attouch, J. Bolte, P. Redont, M. Teboulle
    Singular Riemannian Barrier Methods and Gradient Projected Dynamical Systems for Constrained Optimization
    Optimization, 53, (2004), 435-–454


  • J. Kogan, M. Teboulle, C. Nicholas
    Data Driven similarity measures for k-means like clustering algorithms
    Information Retrival, 8, (2005), 331–-349


  • A. Auslender, M. Teboulle
    Interior projection-like methods for monotone variational inequalities.
    Mathematical Programming, 104, (2005), 39–-68


  • M. Teboulle, J. Kogan
    Deterministic annealing and a k-means type smoothing optimization algorithm
    SIAM Proceedings of Workshop on Clustering High Dimensional Data and its Applications, (2005), 13--22


  • Auslender and M. Teboulle
    Interior gradient and proximal methods in convex and conic optimization
    SIAM J. Optimization, 16, (2006), 697-–725


  • A. Beck and M. Teboulle
    A Linearly Convergent Dual-Based Gradient Projection Algorithm for Quadratically Constrained Convex Minimization
    Mathematics of Operations Research, 31, (2006), 398-–417


  • M. Teboulle, P. Berkhin, I. Dhillon, Y. Guan, and J. Kogan
    Clustering with entropy-like k-means algorithms
    Grouping Multidimensional Data: Recent Advances in Clustering, (J. Kogan, C. Nicholas, and M. Teboulle, (Eds.)), Springer Verlag, NY, (2006), 127--160


  • A. Beck, A. Ben-Tal, M. Teboulle
    Finding a global optimal solution for a quadratically constrained fractional quadratic problem with applications to the regularized total least squares
    SIAM J. Matrix Analysis and Applications, 28, (2006), 425--445


  • M. C. Pinar and M. Teboulle
    On semidefinite bounds for maximization of a non-convex quadratic objective over the l-one unit ball
    RAIRO Operations Research, 40, (2006) 253-265


  • M. Teboulle
    A unified continuous optimization framework for center-based clustering methods
    Journal of Machine Learning Research, 8, (2007) 65-102


  • A. Auslender, P.J.S. Silva, M. Teboulle
    Nonmonotone Projected Gradient Methods Based on Barrier and Euclidean Distances.
    Computational Optimization and Applications, 38, (2007) 305-327


  • A. Ben-Tal and M. Teboulle
    An old-new concept of convex risk measures: the optimized certainty equivalent.
    Mathematical Finance, 17, (2007), 449-476


  • A. Beck, M. Teboulle, Z. Chikishev
    Iterative Minimization Schemes for Solving the Single Source Localization Problem
    SIAM Journal on Optimization, 19 (2008), no. 3, 1397--1416.


  • Y. Eldar, A. Beck, M. Teboulle
    A Minimax Chebyshev Estimator for Bounded Error Estimation
    IEEE Transactions on Signal Processing, Vol. 56, No. 4, (2008), 1388-1397.


  • A. Auslender and M. Teboulle
    Projected Subgradient Methods with Non-Euclidean Distances for Nondifferentiable Convex Minimization and Variational Inequalities
    Mathematical Programming B, Vol. 120, 27-48 (2009).


  • A. Beck and M. Teboulle
    A Convex Optimization Approach for Minimizing the Ratio of Indefnite Quadratic Functions over an Ellipsoid
    Mathematical Programming A, Vol 118, 13-35, (2009).


  • H. Attouch, R. Cominetti and M. Teboulle
    Foreword: Special issue on nonlinear convex optimization and variational inequalities
    Mathematical Programming, Series B, Vol. 116 (2009), 1 --3


  • A. Beck and M. Teboulle
    Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
    SIAM J. Imaging Sciences, Vol. 2 (2009), 183 -- 202


  • A. Beck and M. Teboulle
    Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring
    IEEE Trans. Image Proc. vol. 18, no. 11, November 2009, 2419--2434.


  • L.C. Ceng, M. Teboulle and J.C. Yao
    Weak Convergence of an Iterative Method for Pseudomonotone Variational Inequalities and Fixed-Point Problems
    Journal of Optimization Theory and Applications Volume 146, Number 1, 19-31, 2010.


  • A. Beck and M. Teboulle
    Gradient-Based Algorithms with Applications in Signal Recovery Problems PDF
    In Convex Optimization in Signal Processing and Communications, D. Palomar and Y. Eldar Eds., pp. 33--88. Cambribge University Press, 2010.


  • A. Beck and M. Teboulle
    On Minimizing Quadratically Constrained Ratio of Two Quadratic Functions
    Journal of Convex Analysis 17(2010), No. 3&4, 789--804.


  • Alfred Auslender, Ron Shefi and Marc Teboulle
    A Moving Balls Approximation Method for a Class of Smooth Constrained Minimization Problems
    SIAM J. Optim. 20, 2010, pp. 3232-3259.


  • Ronny Luss and Marc Teboulle
    Convex Approximations to Sparse PCA via Lagrangian Duality
    Operations Research Letters, 39(1), 2011, pp. 57-61.


  • A. Beck and M. Teboulle
    A Linearly Convergent Algorithm for Solving a Class of Nonconvex/Affine Feasibility Problems
    In Fixed-Point Algorithms for Inverse Problems in Science and Engineering, Eds H. Bauschke et al., Springer Optimization and Its Applications, 2011, Volume 49, 33-48.


  • A. Beck, Y. Drori and M. Teboulle
    A new semidefinite programming relaxation scheme for a class of quadratic matrix problems
    Operations Research Letters, 40(4), 2012, pp. 298--302.


  • A. Beck and M. Teboulle
    Smoothing and First Order Methods: A Unified Framework
    SIAM J. Optimization, 22, 2012, pp. 557--580.


  • R. Luss and M. Teboulle
    Conditional Gradient Algorithms for Rank One Matrix Approximations with a Sparsity Constraint
    SIAM Review, 55, 2013, pp. 65--98.


  • Y. Drori and M. Teboulle
    Performance of first-order methods for smooth convex minimization: a novel approach
    Mathematical Programming Series A, 2013.

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