Some of my publications can be downloaded here, some others are not available online. To request any papers in electronic format, please contact me. Here is my google scholar

Working papers and submitted papers


  1. Q. Tran-Dinh, and Zheqi Zhang, Extended Gauss-Newton and Gauss-Newton ADMM algorithms for low-rank matrix optimization, Download at: http://arxiv.org/abs/1606.03358, (Tech. Report), (2016) [pdf].

  2. Q. Tran-Dinh, A. Kyrillidis, and V. Cevher, A single-phase, proximal path-following framework, Download at: http://arxiv.org/abs/1603.01681, (submitted), (2016) [pdf].

  3. Q. Tran-Dinh, Construction and Iteration-Complexity of Primal Sequences in Alternating Minimization Algorithms, Online at: http://arxiv.org/abs/1511.03305, (submitted), (2015) [pdf].

  4. Q. Tran-Dinh, Adaptive Smoothing Algorithms for Nonsmooth Composite Convex Minimization, Online at: http://arxiv.org/abs/1509.00106, (submitted), (2015) [pdf].

  5. Q. Tran-Dinh and V. Cevher, An optimal first-order primal-dual gap reduction framework for constrained convex optimization, Tech. Report (2015), Online at: http://arxiv.org/pdf/1507.06243.pdf, (submitted), (2015) [pdf].

  6. Q. Tran-Dinh and V. Cevher, Splitting the Smoothed Primal-Dual Gap: Optimal Alternating Direction Methods, Tech. Report (2015), Online at: http://arxiv.org/pdf/1507.03734.pdf, (submitted), (2015) [pdf].

  7. Q. Tran-Dinh and Volkan Cevher, A Primal-Dual Algorithmic Framework for Constrained Convex Minimization. LIONS Tech. Report EPFL-REPORT-199844, (2014). Available at http://arxiv.org/abs/1406.5403 [pdf]

Journal papers (published and accepted)


  1. A. Patrascu, I. Necoara, and Q. Tran-Dinh, Adaptive inexact fast augmented Lagrangian methods for constrained convex optimization, Optimization Leters, (2016), Online at: http://link.springer.com/article/10.1007%2Fs11590-016-1024-6[pdf]. Download at: http://arxiv.org/pdf/1505.03175.pdf, (submitted), (2015) [pdf].

  2. Q. Tran-Dinh, I. Necoara, M. Diehl, Fast inexact distributed optimization algorithms for separable convex optimization, Optimization, (2015) (in press), DOI:10.1080/02331934.2015.1044898. Online at: http://arxiv.org/abs/1107.5841, (2012) [pdf].

  3. Q. Tran-Dinh, Anastasios Kyrillidis and Volkan Cevher, An Inexact Proximal Path-Following Algorithm for Constrained Convex Minimization, SIAM J. Optimization, vol. 24, no. 4, pages 1718--1745, (2014), Preprint is available at: http://arxiv.org/abs/1311.1756 [pdf]

  4. Q. Tran-Dinh, Anastasios Kyrillidis and Volkan Cevher, Composite Self-concordant minimization, Journal of Machine Learning Research, 16(Mar):371−416, 2015. Available at: http://www.jmlr.org/papers/volume16/trandihn15a/trandihn15a.pdf [pdf]. Preprint is available at: http://arxiv.org/abs/1308.2867 [pdf]

  5. V. Nedelcu, I. Necoara and Q. Tran-Dinh, Computational Complexity of Inexact Gradient Augmented Lagrangian Methods: Application to Constrained MPC, SIAM J. Optimization and Control, vol. 52, no. 5, pages 3109--3134, 2014. Online at: http://arxiv.org/abs/1302.4355 [pdf].

  6. Michael B. McCoy, Volkan Cevher, Quoc Tran-Dinh, Afsaneh Asaei, Luca Baldassarre, Convexity in source separation: Models, geometry, and algorithms, Signal Processing Magazine, Vol. 31, No. 3, pages 87--95. Available at: http://arxiv.org/abs/1311.0258 [pdf]

  7. Debrouwere, F., Van Loock, W., Pipeleers, G., Tran-Dinh, Q., Diehl, M., De Schutter, J., Swevers, J.; Time-Optimal Path Following for Robots with Convex-Concave Constraints using Sequential Convex Programming. IEEE Transactions on Robotics, Vol. 99, No. xx, pp. 1--11, 2013, Available at: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=arnumber=6634254. [pdf]

  8. Q. Tran-Dinh, I. Necoara and M. Diehl, Path-Following Gradient-Based Decomposition Algorithms For Separable Convex Optimization, Journal of Global Optimization, Vol. 59, No. 1, pp. 59--80, Online at: http://link.springer.com/article/10.1007%2Fs10898-013-0085-7, [pdf] or at [pdf].

  9. Signoretto M., Tran-Dinh Q., De Lathauwer L., Suykens J.A.K. Learning with Tensors: a Framework Based on Convex Optimization and Spectral Regularization, Machine Learning, DOI 10.1007/s10994-013-5366-3, (2013). Available at: http://link.springer.com/article/10.1007%2Fs10994-013-5366-3

  10. Q. Tran-Dinh, I. Necoara, C. Savorgnan and M. Diehl, An inexact perturbed path-following method for Lagrangian decomposition in large-scale separable convex optimization, SIAM J. Optimization, vol. 23, no. 1 (2013), pp. 95-125. Available at: http://arxiv.org/abs/1109.3323, 2011 [pdf].

  11. Q. Tran-Dinh, C. Savorgnan and M. Diehl, Adjoint-based predictor-corrector sequential convex programming for parametric nonlinear optimization, SIAM Journal on Optimization, Vol. 22, No. 4, pp. 1258--1284, , 2012 [pdf] or at [pdf].

  12. Q. Tran-Dinh, C. Savorgnan and M. Diehl, Combining Lagrangian Decomposition and Excessive Gap Smoothing Technique for Solving Large-Scale Separable Convex Optimization Problems, Computational Optimization and Applications, 2012 [online], [pdf] or at [http://arxiv.org/abs/1105.5427].

  13. Q. Tran-Dinh, S. Gumussoy, W. Michiels and M. Diehl, Combining convex-concave decompositions and linearization approaches for solving BMIs, with application to static output feedback, IEEE Transactions on Automatic Control, vol. 57, No. 6, pp. 1377--1390, 2012 (regular paper)[pdf] Online at: [http://arxiv.org/abs/1109.3320]

  14. Q. Tran-Dinh and M. Le Dung, A splitting proximal point method for Nash-Cournot equilibrium models involving nonconvex cost functions , Journal of Nonlinear and Convex Analysis, Vol. 12, No. 3, pp. 519--533, 2011 [pdf].

  15. M. Le Dung, Q. Tran-Dinh, H.A. Le Thi and T. Pham Dinh, Decomposition algorithms for globally solving mathematical programs with affine equilibrium constraints, Acta Mathematica Vietnamica, Vol. 37, No. 2, pp. 201--218, 2012 [pdf].

  16. Q. Tran-Dinh, A. Pham Ngoc and M. Le Dung, Dual extragradient algorithms extended to equilibrium problems, Journal of Global Optimization, Vol. 52, No. 1, pp. 139--159, (2012) [pdf].

  17. Q. Tran-Dinh and M. Le Dung, Iterative methods for solving monotone equilibrium problems via dual gap functions, Computational Optimization and Applications, Vol. 51, No. 2, pp. 709--728 , DOI 10.1007/s10589-010-9360-4 (2010) [pdf].

  18. M. Le Dung and Q. Tran-Dinh, One step from DC optimization to DC mixed variational inequalities, Optimization, Vol. 59, No 1, 63-76 (2010) [pdf].

  19. M. Le Dung and Q. Tran-Dinh, Regularization algorithms for solving monotone equilibrium problems, Journal of Optimization Theory and Applications, Vol. 124, 185-204 (2009) [pdf].

  20. Q. Tran-Dinh, M. Le Dung and H. Nguyen Van, Extragradient algorithms extended to equilibrium problems, Optimization, Vol.57, 749-776 (2008) [pdf].

Conference papers published in full in proceedings


  1. G. Odor, Y.-H. Li, A. Yurtsever, Y.-P. Hsieh and Q. Tran Dinh et al. Frank-Wolfe Works for Non-Lipschitz Continuous Gradient Objectives: Scalable Poisson Phase Retrieval, in 41st IEEE International Conference on Acoustics, Speech and Signal Processing, 2016. (ICASSP2016), 2016., Online at: http://arxiv.org/abs/1602.00724, (2015) [pdf].

  2. Anastasios Kyrillidis, Bubacarr Bah, Rouzbeh Hasheminezhad, Quoc Tran Dinh, Luca Baldassarre, Volkan Cevher Convex block-sparse linear regression with expanders, provably, Accepted for publication in The 19th International Conference on Artificial Intelligence and Statistics (IASTATS2016), 2016, Spain.

  3. A. Yurtsever, Q. Tran-Dinh, and V. Cevher, Universal Primal-Dual Proximal-Gradient Methods, Accepted for publication in 29th Ann. Conf. Neural Information Processing Systems (NIPS2015), 2015., Online at: http://arxiv.org/pdf/1502.03123.pdf, (2015) [pdf].

  4. Gozcu, B. and Baldassarre, L., Tran-Dinh, Q., Aprile, C. and Cevher, V., A Primal-dual Framework For Mixtures Of Regularisers, Proceedings of the 23rd European Signal Processing Conference (EUSIPCO 2015), (to appear), 2015.

  5. S. Srivastava, V. Cevher, Q. Tran-Dinh and D. B. Dunson. WASP: Scalable Bayes via barycenters of subset posteriors. Appearing in Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (AISTATS) 2015, San Diego, CA, USA. JMLR: W&CP volume 38, (2015).

  6. Q. Tran-Dinh and V. Cevher. Constrained convex minimization via model-based excessive gap. Accepted for publication in the Proceedings of the annual conference on Neural Information Processing Systems Foundation (NIPS), Montreal, Canada, (2014).

  7. A. Kyrillidis, R.K. Mahabadi, Q. Tran-Dinh and V. Cevher. Scalable sparse covariance estimation via self-concordance. Accepted for publication in the Proceedings of the 28th AAAI Conference on Artificial Intelligence, Quebec, Canada, (2014).

  8. Q. Tran-Dinh, Y.H. Li and V. Cevher. Barrier Smoothing for Nonsmooth Convex Minimization. Accepted for publication in Proceedings of the 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Florence, Italy, (2014).

  9. Q. Tran-Dinh, I. Necoara and M. Diehl, A dual decomposition algorithm for separable nonconvex optimization using the penalty function framework. Proceedings of the conference on Decision and Control, pp. 2372--2377, (2013). Online at [pdf]

  10. Q. Tran-Dinh, Anastasios Kyrillidis and Volkan Cevher, A proximal Newton framework for composite minimization: Graph learning without Cholesky decompositions and matrix inversions, In Proc. of the International Conference on Machine Learning, Online at: http://icml.cc/2013/?page_id=876,(2013) [pdf].

  11. Debrouwere, F., Van Loock, W., Pipeleers, G., Tran-Dinh, Q., Diehl, M., De Schutter, J., Swevers, J. (2013). Time-Optimal Path Following for Robots with Trajectory Jerk Constraints using Sequential Convex Programming. IEEE International Conference on Robotics and Automation (ICRA). IEEE International Conference on Robotics and Automation. Karlsruhe, Germany, 6-10 May 2013 (pp. 1908-1913).

  12. Debrouwere, F., Van Loock, W., Pipeleers, G., Tran-Dinh, Q., Diehl, M., De Schutter, J., Swevers, J.: Optimal Robot Path Following for Minimal Time versus Energy Loss Trade-Off using Sequential Convex Programming. IEEE International Conference on Mechatronics. Vicenza, Italy, February 27 - March 1 2013.

  13. Debrouwere, F., Van Loock, W., Pipeleers, G., Tran-Dinh, Q., Diehl, M., De Schutter, J., Swevers, J., Time-optimal robot path following with Cartesian acceleration constraints: a convex optimization approach. The 13th Mechatronics Forum International Conference: Vol. 2 (3). Mechatronics Forum International Conference. Linz, Austria, 17-19 September 2012 (pp. 469-475).

  14. Q. Tran-Dinh, W. Michiels, S. Gros and M. Diehl, An inner convex approximation algorithm for BMI optimization and applications in control, Accepted for publication in Proc. of the 51st IEEE Conference on Decision and Control, Online at: http://arxiv.org/abs/1202.5488,(2012) [pdf].

  15. Q. Tran-Dinh, C. Savorgnan and M. Diehl, Real-Time Sequential Convex Programming for Nonlinear Model Predictive Control and Application to a Hydro-Power Plant , Proc. of the 50th IEEE Conference on Decision and Control, Orlando, Florida, USA, 2011 [pdf] or at [pdf].

  16. I. Necoara, C. Savorgnan, Q. Tran-Dinh, J.A.K. Suykens, M. Diehl, Distributed Nonlinear Optimal Control Using Sequential Convex Programming and Smoothing Techniques, Proc. of the 48th IEEE Conference on Decision and Control, Shanghai, China, 2009. Online at [pdf]

  17. Q. Tran-Dinh and M. Diehl, An application of sequential convex programming methods to time optimal trajectory planning of a car motion, Proc. of the 48th IEEE Conference on Decision and Control, Shanghai, China, 4366--4371, 2009 [pdf] or at [pdf].

Book chapters


  1. Q. Tran-Dinh and M. Diehl, Local convergence of sequential convex programming for nonlinear programming, In: Diehl, M.; Glineur, F.; Jarlebring, E.; Michiels, W. (Eds.), Recent advances in optimization and its application in engineering, Springer-Verlag, pp. 93--102 (2010) [pdf] or [pdf].

  2. Q. Tran-Dinh, C. Savorgnan and M. Diehl, Real-time sequential convex programming for optimal control applications, In: H.G. Bock, P. Hoang Xuan et al (Eds.), Modeling, Simulation and Optimization of Complex Processes, Springer-Verlag, (2009). Online at http://arxiv.org/abs/1105.3427([pdf]).

  3. Kyrillidis, Anastasios and Baldassarre, Luca and El Halabi, Marwa and Tran-Dinh, Quoc and Cevher, Volkan, Structured Sparsity: Discrete and Convex Approaches, Compressed Sensing and its Applications: MATHEON Workshop 2013, pp. 341--378, (2015), Birkhauser.

  4. Q. Tran-Dinh, Y.-H. Li, and V. Cevher, Composite Convex Minimization Involving Self-concordant-Like Cost Functions, in: Modelling, Computation and Optimization in Information Systems and Management Sciences, Advances in Intelligent Systems and Computing, Vol. 359, 2015, pp. 155--168. Available at: http://link.springer.com/chapter/10.1007/978-3-319-18161-5_14([pdf]), or http://arxiv.org/pdf/1502.01068.pdf([pdf]).

Manuscripts, internal reports and unpublished drafts


  1. Q. Tran-Dinh and M. Diehl, Proximal methods for minimizing the sum of a convex function and a composite function, Online at: http://arxiv.org/abs/1105.0276, (2010) [pdf].

  2. Q. Tran-Dinh and M. Diehl, Sequential Convex Programming Methods for Solving Nonlinear Optimization Problems with DC constraints, Online at: http://arxiv.org/abs/1107.5841, (2009) [pdf].

PhD thesis


  1. Q. Tran-Dinh, Sequential Convex Programming and Decomposition Approaches for Nonlinear Optimization, Online at: https://lirias.kuleuven.be/handle/123456789/359872, November, 2012, [pdf].

    Abstract: This thesis is devoted to studying numerical solution methods for some classes of nonlinear optimization problems. These methods are motivated from the fact that many optimization problems in practice possess certain structures such as convexity, separability and sparsity. Moreover, solving a convex optimization problem is more efficient and reliable than a nonconvex one by using the state- of-the-art of convex optimization algorithms. Exploiting such specific structures and convex optimization techniques can lead to more efficient and reliable solution methods than conventional approaches.
    The content of the thesis is divided into two parts.
    Part I studies the sequential convex programming approach for solving nonconvex optimization problems, both parametric nonlinear programming and nonconvex semidefinite programming. A generic algorithmic framework which we call adjoint- based predictor-corrector sequential convex programming is proposed to treat parametric nonconvex optimization problems with general convex constraints. The algorithm is based on three ingredients, namely sequential convex programming, predictor-corrector path-following and adjoint-based optimization. The stability of the tracking errors between approximation solutions and the true ones is proved. Without parameters, the algorithm collapses to the one which we call the sequential convex programming (SCP) method for solving nonconvex optimization problems. As a special case of SCP, we develop a generalized inner convex approximation method and a generalized convex-concave decomposition algorithm for solving a class of nonconvex semidefinite programming problems. We also show applications of these algorithms in static state/output feedback controller design. Numerical results are benchmarked via several standard numerical examples.
    Part II deals with decomposition approaches for separable optimization, both in the convex and nonconvex case. We develop several decomposition methods for solving separable convex optimization problems. The first class of algorithms is based on two main ingredients, namely smoothing via prox-functions and the excessive gap technique. The convergence of these algorithms is proved and the convergence rate is estimated. Extensions to the strongly convex case and inexact cases are also considered. The second class of algorithms makes use of smoothing techniques via self-concordant barrier functions and a path-following method. The algorithms developed in this part can be implemented in a parallel or distributed fashion. Several algorithmic variants are tested in numerical examples. We also show an application of these algorithms to the nonconvex case. This leads to a two-level decomposition algorithm for solving a class of separable nonconvex optimization problems.
    Keywords: Sequential convex programming, decomposition method, path-following, generalized inner convex approximation, convex-concave decomposition, smoothing technique, parametric optimization, separable convex optimization, parallel and distributed algorithm.

Co-author list


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