MARC TEBOULLE -- LIST OF PUBLICATIONS

THESES
M.Sc., ``Second order optimality conditions for optimization problems with continuum of constraints'' (Technion 1978).
D.Sc., `` Penalty function approaches and duality in stochastic programming with applications in information theory'' (Technion 1985).

BOOKS


REFEREED PAPERS
  1. A. Ben-Tal, M. Teboulle, J. Zowe
    Second order necessary optimality conditions for semi-infinite programming
    In Springer Lecture Notes In Control and Information Sciences (R. Hettich, ed.) 15 (1979), 17-30


  2. J. Hasson, B. Priel, M. Teboulle
    Noise effect on optimal multistage gyrocompassing.
    In Proceedings of the 13th Convention of electrical and Electronic Engineers, IEEE Publication, Israel (1984)


  3. A. Ben-Tal, M. Teboulle
    The duality between expected utility and penalty in stochastic linear programming.
    In Springer Lecture Notes in Control and Information Sciences (F. Archetti et al., eds) 76 (1986), 151-161


  4. A. Ben-Tal, M. Teboulle
    Expected utility, penalty functions and duality in stochastic nonlinear programming
    Management Science 32 (1986), 1445-1466


  5. A. Ben-Tal, M. Teboulle
    Rate distortion theory with generalized information measures via convex programming duality.
    IEEE Transactions of Information Theory IT 32 (1986), 630-641


  6. A. Ben-Tal, M. Teboulle
    Penalty functions and duality in stochastic programming via \phi-divergence functionals.
    Mathematics of Operations Research 12 (1987), 224-240.


  7. A. Ben-Tal, M. Teboulle, A. Charnes
    The role of duality in optimization problems involving entropy functionals.
    J. of Optimization Theory and Applications 58 (1988), 209-223.


  8. A. Ben-Tal, M. Teboulle
    Extension of some results for channel capacity using a generalized information measure.
    J. of Applied Mathematics and Optimization 17 (1988), 121-132.


  9. A. Ben-Tal, J.M.~Borwein, M. Teboulle
    A dual approach to multidimensional $L_{p}$ spectral estimation problems.
    SIAM J. of Control and Optimization 26 (1988), 985-996.


  10. J. Birge, M. Teboulle
    Upper bounds on the expected value of a convex function using gradient and conjugate function information.
    Mathematics of Operations Research 14 (1989), 745-759.


  11. A. Ben-Tal, A.~Charnes, M. Teboulle
    Entropic means.
    J. of Mathematical Analysis and Applications 139 (1989), 537-551.


  12. M. Teboulle
    A simple duality proof for quadratically constrained entropy functionals and extensions to convex constraints.
    SIAM J. of Applied Mathematics 49 (1989), 1845-1850.


  13. A. Ben-Tal, M. Teboulle
    A smoothing technique for nondifferentiable optimization problems.
    Optimization, Springer Lecture Notes in Mathematics (S. Dolecki, ed.), 1405 (1989), 1-11.


  14. M. Teboulle
    Nonlinear perturbations for linear semi-infinite optimization problems.
    Proc.\ of the 29th IEEE Conference on Decision and Control 2477-2478 (1990)


  15. A. Ben-Tal, M. Teboulle
    A geometric property of the least squares solution of linear equation.
    Linear Algebra and Applications 139 (1990), 165-170.


  16. M.S. Gowda, M. Teboulle
    A comparison of constraint qualifications in infinite dimensional convex programming.
    SIAM J. of Control and Optimization 28 (1990), 925-935.


  17. A. Ben-Tal, A. Ben-Israel, M. Teboulle
    Certainty equivalents and information measures: duality and extremal principles.
    J. of Mathematical Analysis and Applications 157 (1991), 211-236.


  18. A. Ben-Tal, M. Teboulle
    Portfolio theory for the recourse certainty equivalent maximizing investor.
    Annals of Operations Research 31 (1991), 479-499.


  19. A. Ben-Tal, M. Teboulle, W.H. Yang
    A least-squares based method for a class of nonsmooth minimization problems with applications in plasticity.
    J. of Applied Mathematics and Optimization 24 (1991), 273-288.


  20. M. Teboulle
    On $\varphi$-divergence and its applications.
    Systems and Management Science by Extremal Methods (f.Y. Phillips, J. Rousseau, eds.), Kluwer Academic Press, chap. 17 (1992), 255-273.


  21. A. Ben-Tal, J.M. Borwein, M. Teboulle
    Spectral estimation via convex programming.
    Systems and Management Science by Extremal Methods (f.Y. Phillips, J. Rousseau, eds.), Kluwer Academic Press, chap. 18 (1992), 275-289.


  22. M. Teboulle
    Entropic proximal mappings with applications to nonlinear programming.
    Mathematics of Operations Research 17 (1992), 670--690.


  23. A.N. Iusem, M. Teboulle
    A primal-dual iterative algorithm for a maximum likelihood estimation problem.
    J. of Computational Statistics and Data Analysis 14 (1992), 443-456.


  24. G. Chen, M. Teboulle
    Convergence analysis of a proximal-like minimization algorithm using Bregman's function.
    SIAM J. of Optimization 3 (1993), 538-543.


  25. M. Teboulle, I. Vajda
    Convergence of best $\varphi$-entropy estimates.
    IEEE Transactions on Information Theory 39 (1993), 297-301.


  26. M. Teboulle, J. Kogan
    Applications of optimization methods to robust stability of linear systems.
    J. of Optimization Theory and Applications 81 (1994), 169-192.


  27. A.N. Iusem, M. Teboulle
    A regularized dual-based iterative method for a class of image reconstruction problems.
    Inverse Problems 9 (1993), 679-696.


  28. A.N. Iusem, B.F. Svaiter, M. Teboulle
    Entropy-like methods in convex programming.
    Mathematics of Operations Research 19 (1994), 790-814.


  29. G. Chen, M. Teboulle
    A proximal-based decomposition method for convex minimization problems.
    Mathematical Programming 64 (1994), 81-101.


  30. A.N. Iusem, M. Teboulle
    On the convergence rate of Entropic proximal optimization algorithms.
    Computational and Applied Mathematics 12 (1993), 153-168.


  31. A.N. Iusem, M. Teboulle
    Convergence rate analysis of nonquadratic proximal and augmented Lagrangian methods for convex and linear programming.
    Mathematics of Operations Research 20 (1995), 657-677.


  32. M. Hershkovitz, U. Tash, M. Teboulle
    Towards a mathematical formulation of the human grasping quality sense.
    Journal of Robotic Systems 12 (1995), 249-256.


  33. A.N. Iusem, B.F. Svaiter, M. Teboulle
    Multiplicative interior gradient methods for minimization over the nonnegative orthant.
    SIAM J. Control and Optimization 34 (1996), 389-406.


  34. M. Hershkovitz, U. Tash, M. Teboulle, J. Tzelgov
    An optimization model for the human grasping quality sense
    Proceedings on Mechanical Engineering, (1996), 68-72.


  35. A. Ben-Tal, M. Teboulle
    Hidden convexity in some nonconvex quadratically constrained quadratic programming.
    Mathematical Programming 72 (1996), 51-63.


  36. A. Ben-Tal, M. Teboulle
    A conjugate duality scheme generating a new class of differentiable duals.
    SIAM J. on Optimization, 6 (1996), 617-625.


  37. R. Polyak, M. Teboulle
    Nonlinear rescaling and proximal-like methods in convex optimization.
    Mathematical Programming 76 (1997), 265-284.


  38. M. Hershkovitz, U. Tash, M. Teboulle, J. Tzelgov
    Experimental Validation of an Optimization Formulation of the Human Grasping Quality Sense
    Journal of Robotic Systems, 14 (1997), 743-766.


  39. M. Teboulle
    Convergence of Proximal-lile Algorithms.
    SIAM J. Optimization 7 (1997), 1069-1083.


  40. M. Hershkovitz and M. Teboulle
    Sensitivity analysis for a class of robotic grasping quality functionals.
    Robotica 16, (1998), 227-235.


  41. M. Doljansky, M. Teboulle
    An interior proximal algorithm and the exponential multiplier method for semidefinite programming.
    SIAM J. Optimization, 9, (1998), 1-13.


  42. A. Auslender, M. Teboulle and S. Ben-Tiba
    A logarithmic-quadratic proximal method for variational inequalities.
    J. of Computational Optimization and Applications, 12, (1999), 31-40.


  43. A. Auslender, M. Teboulle and S. Ben-Tiba
    Interior proximal and multiplier methods based on second order homogeneous kernels.
    Mathematics of Operations Research, 24, (1999), 645-668.


  44. A. Auslender, M. Teboulle and S. Ben-Tiba
    Coupling the logarithmic-quadratic proximal method and the block nonlinear Gauss-Seidel algorithm for linearly constrained convex minimization.
    Ill-Posed Problems Variational Problems and Regularization Techniques, Lecture Notes in Economics and Mathematical Systems, 477, (1999), 35-47.


  45. A. Beck and M. Teboulle
    Global optimality conditions for quadratic optimization problems with binary constraints,
    SIAM J. Optimization, 11, (2000), 179--188.


  46. A. Auslender and M. Teboulle
    Lagrangian duality and related multiplier methods for variational inequalities.
    SIAM J. Optimization, {\bf 10}, (2000), 1097--1115


  47. A. Beck and M. Teboulle
    A Probabilistic result for the max-cut problem on random graphs.
    Operations Research Letters, 27, (2000), 209-214.


  48. M. Teboulle
    Lagrangian Multiplier Methods in Convex Programming.
    In Encyclopedia of Optimization, Kluwer Academic Press, (2001).


  49. A. Auslender and M. Teboulle
    Entropic proximal decomposition methods for convex programs and variational inequalities.
    Mathematical Programming, 91, (2001), 33-47. <\li>

  50. A. Auslender and M. Teboulle
    A logarithmic-quadratic projection method for convex feasibility problems.
    Studies in Computational Mathematics, 8, (2001), 1-10.


  51. J. Kogan , M. Teboulle and C.Nicholas
    The entropic geometric means algorithm: an approach to building small clusters for large text datasets.
    IEEE Proceedings of Workshop on Clustering Large Data Sets, (2003), 63--71.


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


  53. 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


  54. 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).


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


  56. 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.


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


  58. 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.


  59. 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


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


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


  62. 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


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


  64. 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


  65. 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


  66. 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


  67. 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


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


  69. 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


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


  71. 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.


  72. 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.


  73. 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).


  74. 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).


  75. 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


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


  77. 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.


  78. 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.


  79. 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.


  80. 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.


  81. 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.


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


  83. 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.


  84. 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.


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


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


  87. 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.


  88. 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.


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


  90. 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 .


  91. 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 .


  92. 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 .


  93. 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 .


  94. 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.


  95. 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.


  96. 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.


  97. 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.


  98. 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.


  99. 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.


  100. 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.


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


  102. 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.


  103. 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.


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


  105. 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.


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


  107. 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.


  108. 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.


  109. 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.


  110. 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.


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


  112. 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, 193, (2022), 324-353.


  113. S. Sabach and M. Teboulle. Faster Lagrangians Based Methods in Convex Optimization.
    SIAM J. Optimization, 32, (2022), 204-227.


  114. M. Teboulle and Y. Vaisbourd. An elementary approach to tight worst case complexity analysis of gradient based methods.
    Mathematical Programming, Series A. (2023). To appear.


  115. N. Hallak and M. Teboulle. An Adaptive Lagrangian-Based Scheme for Nonconvex Composite Optimization.
    Mathematics of Operations Research (2023). To appear.


    116. Updated -- September 2023



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