• 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.

  • A. Beck, A. Nedich, A. Ozdaglar, and M. Teboulle
    An O(1/k) Gradient Method for Network Resource Allocation Problems
    IEEE Transactions on Control of Network Systems, Volume 1, 2014, pp. 64--73.

  • Y. Drori and M. Teboulle
    Performance of first-order methods for smooth convex minimization: a novel approach
    Mathematical Programming, Series A, Volume 145, 2014, pp 451-482.

  • A. Beck and M. Teboulle
    A fast dual proximal gradient algorithm for convex minimization and applications.
    Operations Research Letters, 42, 2014, pp. 1–6.

  • J. Bolte, S. Sabach and M. Teboulle
    Proximal alternating linearized minimization for nonconvex and nonsmooth problems
    Mathematical Programming, Series A, Volume 146, 2014, pp 459-494 .

  • R. Shefi and M. Teboulle
    Rate of Convergence Analysis of Decmposition Methods Based on the Proximal Method of Multipliers for Convex Minimization
    SIAM J. Optimization, Volume 24, 2014, pp 269--297 .

  • Y. Drori, S. Sabach and M. Teboulle
    A simple algorithm for a class of nonsmooth convex–concave saddle-point problems
    Operation Research Letters, Volume 43, Issue 2, March 2015, Pages 209–214 .

  • H. Bauschke, J. Bolte, and M. Teboulle
    A Descent Lemma Beyond Lipschitz Gradient Continuity: First-Order Methods Revisited and Applications
    Mathematics of Operation Research, August 2016, Pages 1–19 .

  • R. Shefi and M. Teboulle
    On the rate of convergence of the proximal alternating linearized minimization algorithm for convex problems
    EURO Journal on Computational Optimization, 2016, Volume 4, Issue 1, pp 27–46 .

  • Y. Drori and M. Teboulle
    An Optimal Variant of Kelley's Cutting Plane Method
    Mathematical Programming, Series A, 2016, Volume 160, Issue 1, pp 321-351.

  • A. Beck, S. Sabach and M. Teboulle
    An Alternating Semiproximal Method for Nonconvex Regularized Structured Total Least Squares Problems
    SIAM J. Matrix Analysis and Applications, 2016, Vol. 37, No. 3, pp. 1129–1150.

  • R. Shefi and M. Teboulle
    A dual method for minimizing a nonsmooth objective over one smooth inequality constraint
    Mathematical Programming, Series A, 2016, Volume 159, Issue 1, pp 137–164.

  • Y. Drori and M. Teboulle
    An Optimal Variant of Kelley's Cutting-Plane Method
    Mathematical Programming, Series A, Volume 160, Issue 2, (2016), pp. 321-351.

  • H. Bauschke, J. Bolte and M. Teboulle
    A descent Lemma beyond Lipschitz gradient continuity: First-order methods revisited and applications
    Mathematics of Operations Research, Vol. 42, (2017), pp. 330--348.

  • R. Luke, S. Sabach, M. Teboulle and K. Zatlawy
    A simple globally convergent algorithm for the nonsmooth nonconvex single source localization problem
    Journal of Global Optimization, Volume 69, issue 4, (2017), pp. 889--909.

  • J. Bolte, S. Sabach and M. Teboulle. Nonconvex Lagrangian-Based Optimization: Monitoring Schemes and Global Convergence
    Mathematics of Operations Research, Vol. 43, (2018) pp.1210--1232.

  • J. Bolte, S. Sabach, M. Teboulle and Y. Vaisbourd
    First order methods beyond convexity and Lipschitz gradient continuity with applications to quadratic inverse problems
    SIAM J. Optimization, Vol. 28, (2018), pp. 2131--2151.

  • M. Teboulle
    A simplified view of first order methods for optimization
    Mathematical Programming, Volume 170, (2018), pp 67–96.

  • S. Sabach, M. Teboulle and S. Voldman. A smoothing alternating minimization-based algorithm for clustering with sum-min of Euclidean norms.
    Pure Applied Functional Analysis, 3(4), (2018), pp. 653--679.

  • H. Bauschke, J. Bolte, C. Jiawei, M. Teboulle, and X. Wang. On Linear Convergence of Non- Euclidean Gradient Methods without Strong Convexity and Lipschitz Gradient Continuity.
    Journal of Optimization Theory and Applications, 182, (2019), 1068--1087.

  • N. Hallak and M. Teboulle. A non-Euclidean gradient descent method with sketching for unconstrained matrix minimization.
    Operations Research Letters, 47, (2019), 421--426.

  • D. R. Luke, S. Sabach and M. Teboulle. Optimization on Spheres: Models and Proximal Algorithms with Computational Performance Comparisons.
    SIAM J. Mathematics of Data Science, Vol. 1, (2019) 408--445.

  • R. Luke, M. Teboulle, and N. Thao.
    Necessary conditions for linear convergence of iterated expansive, set-valued mappings
    Mathematical Programming, 180, (2020), pp. 1--31.

  • S. Sabach and M. Teboulle. Lagrangian Methods for Composite Optimization.
    Handbook of Numerical Analysis, Volume 20, (2019), 401-436.

  • M. Teboulle and Y. Vaisbourd. Novel Proximal Gradient Methods for Nonnegative Matrix Fac- torization with Sparsity Constraints.
    SIAM J. Imaging Sciences, 13, (2020), 381--421.

  • N. Hallak and M. Teboulle. Finding second-order stationary points in constrained minimization: a feasible direction approach.
    Journal of Optimization Theory and Applications, 186, (2020), 480–503.
  • E. Cohen, S. Sabach and M. Teboulle. Non-Euclidean proximal methods for convex-concave saddle point problems.
    J. of Applied and Numerical Optimization, 3, (2021), 43–60.

  • A. Beck and M. Teboulle. Dual Randomized Coordinate Descent Method for Solving a Class of Nonconvex Problems.
    SIAM J. Optimization, 31, (2021), 1877–1896.

  • E. Cohen, N. Hallak and M. Teboulle. A Dynamic Alternating Direction of Multipliers for Nonconvex Minimization with Nonlinear Functional Equality Constraints.
    J. of Optimization Theory and Applications. (2021) Published online Sept. 2021.

  • S. Sabach and M. Teboulle. Faster Lagrangians Based Methods in Convex Optimization.
    SIAM J. Optimization, (2022).

  • Updated -- Sept 2021

    Back to homepage