### 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).

### 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.