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
 A. BenTal, M. Teboulle, J. Zowe
Second order necessary optimality conditions for semiinfinite
programming
In Springer Lecture Notes In Control and Information Sciences
(R. Hettich, ed.) 15 (1979), 1730

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)
 A. BenTal, 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), 151161
 A. BenTal, M. Teboulle
Expected utility, penalty functions and duality in
stochastic nonlinear programming
Management Science 32 (1986), 14451466
 A. BenTal, M. Teboulle
Rate distortion theory with generalized information measures via
convex programming duality.
IEEE Transactions of Information Theory IT 32 (1986), 630641
 A. BenTal, M. Teboulle
Penalty functions and duality in stochastic programming via
\phidivergence functionals.
Mathematics of Operations Research 12 (1987), 224240.
 A. BenTal, M. Teboulle, A. Charnes
The role of duality in optimization problems involving entropy
functionals.
J. of Optimization Theory and Applications 58 (1988), 209223.
 A. BenTal, M. Teboulle
Extension of some results for channel capacity using a generalized
information measure.
J. of Applied Mathematics and Optimization 17 (1988), 121132.
 A. BenTal, J.M.~Borwein, M. Teboulle
A dual approach to multidimensional $L_{p}$ spectral estimation
problems.
SIAM J. of Control and Optimization 26 (1988), 985996.
 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), 745759.
 A. BenTal, A.~Charnes, M. Teboulle
Entropic means.
J. of Mathematical Analysis and Applications
139 (1989), 537551.
 M. Teboulle
A simple duality proof for quadratically constrained
entropy functionals and extensions to convex constraints.
SIAM J. of Applied Mathematics 49 (1989), 18451850.
 A. BenTal, M. Teboulle
A smoothing technique for nondifferentiable optimization
problems.
Optimization, Springer Lecture Notes in Mathematics (S. Dolecki, ed.),
1405 (1989), 111.
 M. Teboulle
Nonlinear perturbations for linear semiinfinite optimization
problems.
Proc.\ of the 29th IEEE Conference on Decision and Control
24772478 (1990)
 A. BenTal, M. Teboulle
A geometric property of the least squares solution of linear
equation.
Linear Algebra and Applications 139 (1990), 165170.
 M.S. Gowda, M. Teboulle
A comparison of constraint qualifications in infinite dimensional
convex programming.
SIAM J. of Control and Optimization 28 (1990), 925935.
 A. BenTal, A. BenIsrael, M. Teboulle
Certainty equivalents and information measures:
duality and extremal principles.
J. of Mathematical Analysis and
Applications 157 (1991), 211236.
 A. BenTal, M. Teboulle
Portfolio theory for the recourse certainty
equivalent maximizing investor.
Annals of Operations Research 31 (1991), 479499.
 A. BenTal, M. Teboulle, W.H. Yang
A leastsquares based method for a class of nonsmooth minimization
problems with applications in plasticity.
J. of Applied Mathematics and Optimization 24 (1991), 273288.
 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), 255273.
 A. BenTal, 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), 275289.
 M. Teboulle
Entropic proximal mappings with applications to
nonlinear programming.
Mathematics of Operations Research 17 (1992), 670690.
 A.N. Iusem, M. Teboulle
A primaldual iterative algorithm for a
maximum likelihood estimation problem.
J. of Computational Statistics and Data Analysis 14 (1992), 443456.
 G. Chen, M. Teboulle
Convergence analysis of a proximallike minimization algorithm using
Bregman's function.
SIAM J. of Optimization 3 (1993), 538543.
 M. Teboulle, I. Vajda
Convergence of best $\varphi$entropy estimates.
IEEE Transactions on Information Theory 39 (1993), 297301.
 M. Teboulle, J. Kogan
Applications of optimization methods to robust stability of linear
systems.
J. of Optimization Theory and Applications 81 (1994), 169192.
 A.N. Iusem, M. Teboulle
A regularized dualbased iterative method for a class of image
reconstruction problems.
Inverse Problems 9 (1993), 679696.
 A.N. Iusem, B.F. Svaiter, M. Teboulle
Entropylike methods in convex programming.
Mathematics of Operations Research 19 (1994), 790814.
 G. Chen, M. Teboulle
A proximalbased decomposition method for convex minimization
problems.
Mathematical Programming 64 (1994), 81101.
 A.N. Iusem, M. Teboulle
On the convergence rate of Entropic proximal optimization
algorithms.
Computational and Applied Mathematics 12 (1993), 153168.
 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), 657677.
 M. Hershkovitz, U. Tash, M. Teboulle
Towards a mathematical formulation of the
human grasping quality sense.
Journal of Robotic Systems 12 (1995), 249256.
 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), 389406.
 M. Hershkovitz, U. Tash, M. Teboulle, J. Tzelgov
An optimization model for the human grasping quality sense
Proceedings on Mechanical Engineering, (1996), 6872.
 A. BenTal, M. Teboulle
Hidden convexity in some nonconvex quadratically constrained
quadratic programming.
Mathematical Programming 72 (1996), 5163.
 A. BenTal, M. Teboulle
A conjugate duality scheme generating a new class of differentiable
duals.
SIAM J. on Optimization, 6 (1996), 617625.
 R. Polyak, M. Teboulle
Nonlinear rescaling and proximallike methods in convex
optimization.
Mathematical Programming 76 (1997), 265284.
 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), 743766.
 M. Teboulle
Convergence of Proximallile Algorithms.
SIAM J. Optimization 7 (1997), 10691083.
 M. Hershkovitz and M. Teboulle
Sensitivity analysis for a class of robotic grasping quality functionals.
Robotica 16, (1998), 227235.
 M. Doljansky, M. Teboulle
An interior proximal algorithm and the exponential multiplier method
for semidefinite programming.
SIAM J. Optimization, 9, (1998), 113.
 A. Auslender, M. Teboulle and S. BenTiba
A logarithmicquadratic proximal method for variational
inequalities.
J. of Computational Optimization and Applications,
12, (1999), 3140.
 A. Auslender, M. Teboulle and S. BenTiba
Interior proximal and multiplier
methods based on second order homogeneous kernels.
Mathematics of Operations Research, 24, (1999), 645668.
 A. Auslender, M. Teboulle and S. BenTiba
Coupling the logarithmicquadratic proximal method and the block
nonlinear GaussSeidel algorithm for linearly constrained convex
minimization.
IllPosed Problems Variational Problems and Regularization
Techniques, Lecture Notes in Economics and Mathematical Systems,
477, (1999), 3547.
 A. Beck and M. Teboulle
Global optimality conditions for quadratic optimization problems with
binary constraints,
SIAM J. Optimization, 11, (2000), 179188.
 A. Auslender and M. Teboulle
Lagrangian duality and related multiplier
methods for variational inequalities.
SIAM J. Optimization, {\bf 10}, (2000), 10971115
 A. Beck and M. Teboulle
A Probabilistic result for the maxcut problem on random graphs.
Operations Research Letters, 27, (2000), 209214.
 M. Teboulle
Lagrangian Multiplier Methods in Convex Programming.
In Encyclopedia of Optimization, Kluwer Academic Press, (2001).
 A. Auslender and M. Teboulle
Entropic proximal
decomposition methods for convex programs and variational inequalities.
Mathematical Programming, 91, (2001), 3347.
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 A. Auslender and M. Teboulle
A logarithmicquadratic projection method for convex feasibility problems.
Studies in Computational Mathematics, 8, (2001), 110.
 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), 6371.
 A. Beck, M.Teboulle
Mirror Descent and Nonlinear Projected Subgradient Methods for
Convex Optimization
Operations Research Letters 31 (2003), 167175
 J. Bolte, M. Teboulle
Barrier operators and associated gradient like dynamical systems
for constrained minimization problems
SIAM J. of Control and Optimization, 42, (2003), 12661292
 A. Auslender, M. Teboulle
The LogQuadratic 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), 377394
 H. Attouch and M. Teboulle
A regularized LotkaVolterra dynamical system as a continuous
proximallike method in optimization
Journal of Optimization Theory and Applications, 121,
( 2004), 541570.
 A. Auslender, M. Teboulle
Interior gradient and epsilonsubgradient descent methods for constrained
convex minimization
Mathematics of Operations research, 29, (2004), 126
 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), 235247.
 A. Attouch, J. Bolte, P. Redont, M. Teboulle
Singular Riemannian Barrier Methods and Gradient Projected Dynamical Systems for
Constrained Optimization
Optimization, 53, (2004), 435454
 J. Kogan, M. Teboulle, C. Nicholas
Data Driven similarity measures for kmeans like clustering algorithms
Information Retrival, 8, (2005), 331349
 A. Auslender, M. Teboulle
Interior projectionlike methods for monotone variational
inequalities.
Mathematical Programming, 104, (2005), 3968

M. Teboulle, J. Kogan
Deterministic annealing and a kmeans type smoothing optimization algorithm
SIAM
Proceedings of Workshop on Clustering High Dimensional Data and
its Applications, (2005), 1322
 Auslender and M. Teboulle
Interior gradient and proximal methods in convex and conic optimization
SIAM J. Optimization, 16, (2006), 697725
 A. Beck and M. Teboulle
A Linearly Convergent DualBased Gradient Projection Algorithm for Quadratically Constrained Convex
Minimization
Mathematics of Operations Research, 31, (2006), 398417
 M. Teboulle, P. Berkhin, I. Dhillon, Y. Guan, and J. Kogan
Clustering with entropylike kmeans algorithms
Grouping Multidimensional Data: Recent Advances in Clustering, (J.
Kogan, C. Nicholas, and M. Teboulle, (Eds.)), Springer Verlag, NY,
(2006), 127160
 A. Beck, A. BenTal, 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), 425445
 M. C. Pinar and M. Teboulle
On semidefinite bounds for maximization of a nonconvex quadratic objective over the lone unit ball
RAIRO Operations Research, 40, (2006) 253265
 M. Teboulle
A unified continuous optimization framework for centerbased clustering methods
Journal of Machine Learning Research, 8, (2007) 65102
 A. Auslender, P.J.S. Silva, M. Teboulle
Nonmonotone Projected Gradient Methods Based on Barrier and
Euclidean Distances.
Computational Optimization and Applications, 38, (2007) 305327
 A. BenTal and M. Teboulle
An oldnew concept of convex risk measures: the optimized
certainty equivalent.
Mathematical Finance, 17, (2007), 449476
 A. Beck, M. Teboulle, Z. Chikishev
Iterative Minimization Schemes for Solving the Single Source Localization Problem
SIAM Journal on Optimization, 19 (2008), no. 3, 13971416.
 Y. Eldar, A. Beck, M. Teboulle
A Minimax Chebyshev Estimator for Bounded
Error Estimation
IEEE Transactions on Signal Processing, Vol. 56, No. 4, (2008), 13881397.
 A. Auslender and M. Teboulle
Projected Subgradient Methods
with NonEuclidean Distances for Nondifferentiable Convex
Minimization and Variational Inequalities
Mathematical
Programming B, Vol. 120, 2748 (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, 1335, (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 ShrinkageThresholding Algorithm for Linear Inverse Problems
SIAM J. Imaging Sciences, Vol. 2 (2009), 183  202
 A. Beck and M. Teboulle
Fast GradientBased Algorithms for Constrained Total Variation Image Denoising and Deblurring
IEEE Trans. Image Proc. vol. 18, no. 11, November 2009, 24192434.
 L.C. Ceng, M. Teboulle and J.C. Yao
Weak Convergence of an Iterative Method
for Pseudomonotone Variational Inequalities
and FixedPoint Problems
Journal of Optimization Theory and Applications
Volume 146, Number 1, 1931, 2010.
 A. Beck and M. Teboulle
GradientBased Algorithms with Applications in Signal Recovery Problems
PDF
In Convex Optimization in Signal Processing and Communications,
D. Palomar and Y. Eldar Eds., pp. 3388. 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, 789804.
 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. 32323259.
 Ronny Luss and Marc Teboulle
Convex Approximations to Sparse PCA via Lagrangian Duality
Operations Research Letters, 39(1), 2011, pp. 5761.
 A. Beck and M. Teboulle
A Linearly Convergent Algorithm for Solving a Class of Nonconvex/Affine Feasibility Problems
In FixedPoint Algorithms for Inverse Problems in Science and Engineering, Eds H. Bauschke et al.,
Springer Optimization and Its Applications, 2011, Volume 49, 3348.
 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. 298302.
 A. Beck and M. Teboulle
Smoothing and First Order Methods: A Unified Framework
SIAM J. Optimization, 22, 2012, pp. 557580.
 R. Luss and M. Teboulle
Conditional Gradient Algorithms for Rank One Matrix Approximations with a Sparsity Constraint
SIAM Review, 55, 2013, pp. 6598.
 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. 6473.
 Y. Drori and M. Teboulle
Performance of firstorder methods for smooth convex minimization: a novel approach
Mathematical Programming, Series A, Volume 145,
2014, pp 451482.
 A. Beck and M. Teboulle
A fast dual proximal gradient algorithm for convex minimization
and applications.
Operations Research Letters, 42, 2014, pp. 16.
 J. Bolte, S. Sabach and M. Teboulle
Proximal alternating linearized minimization for nonconvex and nonsmooth problems
Mathematical Programming, Series A, Volume 146, 2014, pp 459494 .
 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 269297 .
 Y. Drori, S. Sabach and M. Teboulle
A simple algorithm for a class of nonsmooth convexconcave saddlepoint problems
Operation Research Letters, Volume 43, Issue 2, March 2015, Pages 209214 .
 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 2746 .
 Y. Drori and M. Teboulle
An Optimal Variant of Kelley's Cutting Plane Method
Mathematical Programming, Series A,
2016, Volume 160, Issue 1, pp 321351.
 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. 11291150.
 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 137164.
 H. Bauschke, J. Bolte and M. Teboulle
A descent Lemma beyond Lipschitz gradient continuity: Firstorder methods revisited and applications
Mathematics of Operations Research, Vol. 42, (2017), pp. 330348.
 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. 889909.
 J. Bolte, S. Sabach and M. Teboulle. Nonconvex LagrangianBased Optimization: Monitoring
Schemes and Global Convergence
Mathematics of Operations Research, Vol. 43, (2018), pp.12101232.
 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. 21312151.
 M. Teboulle
A simplified view of first order methods for optimization
Mathematical Programming, Volume 170, (2018), pp 6796.
 S. Sabach, M. Teboulle and S. Voldman. A smoothing alternating minimizationbased algorithm
for clustering with summin of Euclidean norms.
Pure Applied Functional Analysis, 3(4), (2018), pp. 653679.
 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), 10681087.
 N. Hallak and M. Teboulle. A nonEuclidean gradient descent method with sketching for
unconstrained matrix minimization.
Operations Research Letters, 47, (2019), 421426.
 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) 408445.
 S. Sabach and M. Teboulle. Lagrangian Methods for Composite Optimization.
Handbook of Numerical Analysis, Volume 20, (2019), 401436.
 R. Luke, M. Teboulle, and N. Thao.
Necessary conditions for linear convergence of iterated
expansive, setvalued mappings
Mathematical Programming, 180, (2020), pp. 131.
 M. Teboulle and Y. Vaisbourd. Novel Proximal Gradient Methods for Nonnegative Matrix Fac
torization with Sparsity Constraints.
SIAM J. Imaging Sciences, 13, (2020), 381421.
 N. Hallak and M. Teboulle. Finding secondorder stationary points in constrained minimization:
a feasible direction approach.
Journal of Optimization Theory and Applications, 186,
(2020), 480503.
 E. Cohen, S. Sabach and M. Teboulle. NonEuclidean proximal methods for convexconcave
saddle point problems.
J. of Applied and Numerical Optimization, 3, (2021), 4360.
 A. Beck and M. Teboulle. Dual Randomized Coordinate Descent Method for Solving a Class
of Nonconvex Problems.
SIAM J. Optimization, 31, (2021), 18771896.
 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), 324353.
 S. Sabach and M. Teboulle. Faster Lagrangians Based Methods in Convex Optimization.
SIAM J. Optimization, 32, (2022), 204227.
 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.
 N. Hallak and M. Teboulle. An Adaptive LagrangianBased Scheme for Nonconvex Composite
Optimization.
Mathematics of Operations Research (2023). To appear.
Updated  September 2023