Mirror Descent and Nonlinear Projected Subgradient Methods for Convex Optimization

Operations Research Letters, 31, (2003), 167-175

Barrier operators and associated gradient like dynamical systems for constrained minimization problems

SIAM J. of Control and Optimization, 42, (2003), 1266-1292

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).

Convergence rate analysis and error bounds for projection algorithms in convex feasibility problems

Optimization and Software, 18, (2003), 377-394

A regularized Lotka-Volterra dynamical system as a continuous proximal-like method in optimization

Journal of Optimization Theory and Applications, 121, ( 2004), 541--570.

Interior gradient and epsilon-subgradient descent methods for constrained convex minimization

Mathematics of Operations research, 29, (2004), 1-26

A conditional gradient method with linear rate of convergence for solving convex linear systems

Mathematical Methods of Operations Research, 59, (2004), 235-247.

Singular Riemannian Barrier Methods and Gradient Projected Dynamical Systems for Constrained Optimization

Optimization, 53, (2004), 435-454

Data Driven similarity measures for k-means like clustering algorithms

Information Retrival, 8, (2005), 331-349

Interior projection-like methods for monotone variational inequalities.

Mathematical Programming, 104, (2005), 39-68

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

Interior gradient and proximal methods in convex and conic optimization

SIAM J. Optimization, 16, (2006), 697-725

A Linearly Convergent Dual-Based Gradient Projection Algorithm for Quadratically Constrained Convex Minimization

Mathematics of Operations Research, 31, (2006), 398-417

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

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

On semidefinite bounds for maximization of a non-convex quadratic objective over the l-one unit ball

RAIRO Operations Research, 40, (2006) 253-265

A unified continuous optimization framework for center-based clustering methods

Journal of Machine Learning Research, 8, (2007) 65-102

Nonmonotone Projected Gradient Methods Based on Barrier and Euclidean Distances.

Computational Optimization and Applications, 38, (2007) 305-327

An old-new concept of convex risk measures: the optimized certainty equivalent.

Mathematical Finance, 17, (2007), 449-476

Iterative Minimization Schemes for Solving the Single Source Localization Problem

SIAM Journal on Optimization, 19 (2008), no. 3, 1397--1416.

A Minimax Chebyshev Estimator for Bounded Error Estimation

IEEE Transactions on Signal Processing, Vol. 56, No. 4, (2008), 1388-1397.

Projected Subgradient Methods with Non-Euclidean Distances for Nondifferentiable Convex Minimization and Variational Inequalities

Mathematical Programming B, Vol. 120, 27-48 (2009).

A Convex Optimization Approach for Minimizing the Ratio of Indefnite Quadratic Functions over an Ellipsoid

Mathematical Programming A, Vol 118, 13-35, (2009).

Foreword: Special issue on nonlinear convex optimization and variational inequalities

Mathematical Programming, Series B, Vol. 116 (2009), 1 --3

Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems

SIAM J. Imaging Sciences, Vol. 2 (2009), 183 -- 202

Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring

IEEE Trans. Image Proc. vol. 18, no. 11, November 2009, 2419--2434.

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.

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.

On Minimizing Quadratically Constrained Ratio of Two Quadratic Functions

Journal of Convex Analysis 17(2010), No. 3&4, 789--804.

A Moving Balls Approximation Method for a Class of Smooth Constrained Minimization Problems

SIAM J. Optim. 20, 2010, pp. 3232-3259.

Convex Approximations to Sparse PCA via Lagrangian Duality

Operations Research Letters, 39(1), 2011, pp. 57-61.

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 new semidefinite programming relaxation scheme for a class of quadratic matrix problems

Operations Research Letters, 40(4), 2012, pp. 298--302.

Smoothing and First Order Methods: A Unified Framework

SIAM J. Optimization, 22, 2012, pp. 557--580.

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.

Performance of first-order methods for smooth convex minimization: a novel approach

Mathematical Programming, Series A, Volume 145, 2014, pp 451-482.

A fast dual proximal gradient algorithm for convex minimization and applications.

Operations Research Letters, 42, 2014, pp. 16.

Proximal alternating linearized minimization for nonconvex and nonsmooth problems

Mathematical Programming, Series A, Volume 146, 2014, pp 459-494 .

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 .

A simple algorithm for a class of nonsmooth convexconcave saddle-point problems

Operation Research Letters, Volume 43, Issue 2, March 2015, Pages 209214 .

A Descent Lemma Beyond Lipschitz Gradient Continuity: First-Order Methods Revisited and Applications

Mathematics of Operation Research, August 2016, Pages 119 .

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 .

An Optimal Variant of Kelley's Cutting Plane Method

Mathematical Programming, Series A, 2016, Volume 160, Issue 1, pp 321-351.

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.

A dual method for minimizing a nonsmooth objective over one smooth inequality constraint

Mathematical Programming, Series A, 2016, Volume 159, Issue 1, pp 137164.

An Optimal Variant of Kelley's Cutting-Plane Method

Mathematical Programming, Series A, Volume 160, Issue 2, (2016), pp. 321-351.

A descent Lemma beyond Lipschitz gradient continuity: First-order methods revisited and applications

Mathematics of Operations Research, Vol. 42, (2017), pp. 330--348.

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.

Mathematics of Operations Research, Vol. 43, (2018) pp.1210--1232.

First order methods beyond convexity and Lipschitz gradient continuity with applications to quadratic inverse problems

SIAM J. Optimization, Vol. 28, (2018), pp. 2131--2151.

A simplified view of first order methods for optimization

Mathematical Programming, Volume 170, (2018), pp 6796.

Pure Applied Functional Analysis, 3(4), (2018), pp. 653--679.

Journal of Optimization Theory and Applications, 182, (2019), 1068--1087.

Operations Research Letters, 47, (2019), 421--426.

SIAM J. Mathematics of Data Science, Vol. 1, (2019) 408--445.

Necessary conditions for linear convergence of iterated expansive, set-valued mappings

Mathematical Programming, 180, (2020), pp. 1--31.

Handbook of Numerical Analysis, Volume 20, (2019), 401-436.

SIAM J. Imaging Sciences, 13, (2020), 381--421.

Journal of Optimization Theory and Applications, 186, (2020), 480503.

J. of Applied and Numerical Optimization, 3, (2021), 4360.

SIAM J. Optimization, 31, (2021), 18771896.

J. of Optimization Theory and Applications. (2021) Published online Sept. 2021.

SIAM J. Optimization, (2022).