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Many applications in computer graphics require complex, highly detailed models. However the level of detail may vary considerably for a given scenario. To control processing time at a low-endâ€¦ (More)

- Marco Signoretto, Dinh Quoc Tran, Lieven De Lathauwer, Johan A. K. Suykens
- Machine Learning
- 2013

We present a framework based on convex optimization and spectral regularization to perform learning when feature observations are multidimensional arrays (tensors). We give a mathematicalâ€¦ (More)

- Dinh Quoc Tran, Suat Gumussoy, Wim Michiels, Moritz Diehl
- IEEE Transactions on Automatic Control
- 2012

A novel optimization method is proposed to minimize a convex function subject to bilinear matrix inequality (BMI) constraints. The key idea is to decompose the bilinear mapping as a differenceâ€¦ (More)

- Frederik Debrouwere, Wannes Van Loock, +4 authors Jan Swevers
- IEEE Transactions on Robotics
- 2013

Time-optimal path following considers the problem of moving along a predetermined geometric path in minimum time. In the case of a robotic manipulator with simplified constraints, a convexâ€¦ (More)

- Ion Necoara, Carlo Savorgnan, Dinh Quoc Tran, Johan A. K. Suykens, Moritz Diehl
- Proceedings of the 48h IEEE Conference onâ€¦
- 2009

We regard a network of coupled nonlinear dynamical systems that we want to control optimally. The cost function is assumed to be separable and convex. The algorithm we propose to address theâ€¦ (More)

- Dinh Quoc Tran, Carlo Savorgnan, Moritz Diehl
- Comp. Opt. and Appl.
- 2013

A new algorithm for solving large-scale convex optimization problems with a separable objective function is proposed. The basic idea is to combine three techniques: Lagrangian dual decomposition,â€¦ (More)

- Dinh Quoc Tran, Ion Necoara, Carlo Savorgnan, Moritz Diehl
- SIAM Journal on Optimization
- 2013

This paper studies an inexact perturbed path-following algorithm in the framework of Lagrangian dual decomposition for solving large-scale separable convex programming problems. Unlike the exactâ€¦ (More)

- Dinh Quoc Tran, Wim Michiels, Sebastien Gros, Moritz Diehl
- 2012 IEEE 51st IEEE Conference on Decision andâ€¦
- 2012

In this work, we propose a new local optimization method to solve a class of nonconvex semidefinite programming (SDP) problems. The basic idea is to approximate the feasible set of the nonconvex SDPâ€¦ (More)

- Dinh Quoc Tran, Carlo Savorgnan, Moritz Diehl
- SIAM Journal on Optimization
- 2012

This paper proposes an algorithmic framework for solving parametric optimization problems which we call adjoint-based predictor-corrector sequential convex programming. After presenting theâ€¦ (More)

- Dinh Quoc Tran, Ion Necoara, Moritz Diehl
- 52nd IEEE Conference on Decision and Control
- 2013

We propose a dual decomposition method for solving separable nonconvex optimization problems that arise e.g. in distributed model predictive control over networks. We first derive a new sequentialâ€¦ (More)