Carlo Savorgnan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Carlo Savorgnan has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Moritz Diehl
Department of Microsystems Engineering (IMTEK) and Department of Mathematics, University of Freiburg, Freiburg, Germany
6 shared publications - Quoc Tran Dinh
Hanoi University of Science
2 shared publications Tran Dinh Quoc
2 shared publications- Ion Necoara
Automatic Control and Systems Engineering Department, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania; and Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy, 050711 Bucharest, Romania
1 shared publication Q. Tran Dinh
1 shared publicationQuoc Tran Dinh
Department of Electrical Engineering (ESAT-SCD) and Optimization in Engineering Center (OPTEC) K.U. Leuven Kasteelpark Arenberg 10 B-3001 Leuven Belgium
1 shared publication
Latest Publications
- 18 November 2012
- Published by Springer Nature in Computational Optimization and Applications
- Vol. 55 (1) , 75-111
Combining Lagrangian decomposition and excessive gap smoothing technique for solving large-scale separable convex optimization problems
- 18 November 2012
- Published by Springer Nature in Computational Optimization and Applications
- Vol. 55 (1) , 75-111
Combining Lagrangian decomposition and excessive gap smoothing technique for solving large-scale separable convex optimization problems
- 21 January 2012
- Published by Springer Nature
- p. 91-102
Real-Time Sequential Convex Programming for Optimal Control Applications
- 21 January 2012
- Published by Springer Nature
- p. 91-102
Real-Time Sequential Convex Programming for Optimal Control Applications
- Open Access
Combining Lagrangian Decomposition and Excessive Gap Smoothing Technique for Solving Large-Scale Separable Convex Optimization Problems
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, excessive gap and smoothing. The main advantage of this algorithm is that it dynamically updates the smoothness parameters which leads to numerically robust performance. The convergence of the algorithm is proved under weak conditions imposed on the original problem. The rate of convergence is $O(\frac{1}{k})$, where $k$ is the iteration counter. In the second part of the paper, the algorithm is coupled with a dual scheme to construct a switching variant of the dual decomposition. We discuss implementation issues and make a theoretical comparison. Numerical examples confirm the theoretical results.Open AccessCombining Lagrangian Decomposition and Excessive Gap Smoothing Technique for Solving Large-Scale Separable Convex Optimization Problems
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, excessive gap and smoothing. The main advantage of this algorithm is that it dynamically updates the smoothness parameters which leads to numerically robust performance. The convergence of the algorithm is proved under weak conditions imposed on the original problem. The rate of convergence is $O(\frac{1}{k})$, where $k$ is the iteration counter. In the second part of the paper, the algorithm is coupled with a dual scheme to construct a switching variant of the dual decomposition. We discuss implementation issues and make a theoretical comparison. Numerical examples confirm the theoretical results. - Open Access
An inexact perturbed path-following method for Lagrangian decomposition in large-scale separable convex optimization
In this paper, we propose an inexact perturbed path-following algorithm in the framework of Lagrangian dual decomposition for solving large-scale structured convex optimization problems. Unlike the exact versions considered in literature, we allow one to solve the primal problem inexactly up to a given accuracy. The inexact perturbed algorithm allows to use both approximate Hessian matrices and approximate gradient vectors to compute Newton-type directions for the dual problem. The algorithm is divided into two phases. The first phase computes an initial point which makes use of inexact perturbed damped Newton-type iterations, while the second one performs the path-following algorithm with inexact perturbed full-step Newton-type iterations. We analyze the convergence of both phases and estimate the worst-case complexity. As a special case, an exact path- following algorithm for Lagrangian relaxation is derived and its worst-case complexity is estimated. This variant possesses some differences compared to the previously known methods. Implementation details are discussed and numerical results are reported.
Open AccessAn inexact perturbed path-following method for Lagrangian decomposition in large-scale separable convex optimization
In this paper, we propose an inexact perturbed path-following algorithm in the framework of Lagrangian dual decomposition for solving large-scale structured convex optimization problems. Unlike the exact versions considered in literature, we allow one to solve the primal problem inexactly up to a given accuracy. The inexact perturbed algorithm allows to use both approximate Hessian matrices and approximate gradient vectors to compute Newton-type directions for the dual problem. The algorithm is divided into two phases. The first phase computes an initial point which makes use of inexact perturbed damped Newton-type iterations, while the second one performs the path-following algorithm with inexact perturbed full-step Newton-type iterations. We analyze the convergence of both phases and estimate the worst-case complexity. As a special case, an exact path- following algorithm for Lagrangian relaxation is derived and its worst-case complexity is estimated. This variant possesses some differences compared to the previously known methods. Implementation details are discussed and numerical results are reported.
- Open Access
Adjoint-based predictor-corrector sequential convex programming for parametric nonlinear optimization
This paper proposes an algorithmic framework for solving parametric optimization problems which we call adjoint-based predictor-corrector sequential convex programming. After presenting the algorithm, we prove a contraction estimate that guarantees the tracking performance of the algorithm. Two variants of this algorithm are investigated. The first one can be used to solve nonlinear programming problems while the second variant is aimed to treat online parametric nonlinear programming problems. The local convergence of these variants is proved. An application to a large-scale benchmark problem that originates from nonlinear model predictive control of a hydro power plant is implemented to examine the performance of the algorithms.
Open AccessAdjoint-based predictor-corrector sequential convex programming for parametric nonlinear optimization
This paper proposes an algorithmic framework for solving parametric optimization problems which we call adjoint-based predictor-corrector sequential convex programming. After presenting the algorithm, we prove a contraction estimate that guarantees the tracking performance of the algorithm. Two variants of this algorithm are investigated. The first one can be used to solve nonlinear programming problems while the second variant is aimed to treat online parametric nonlinear programming problems. The local convergence of these variants is proved. An application to a large-scale benchmark problem that originates from nonlinear model predictive control of a hydro power plant is implemented to examine the performance of the algorithms.
- Open Access
Real-Time Sequential Convex Programming for Optimal Control Applications
This paper proposes real-time sequential convex programming (RTSCP), a method for solving a sequence of nonlinear optimization problems depending on an online parameter. We provide a contraction estimate for the proposed method and, as a byproduct, a new proof of the local convergence of sequential convex programming. The approach is illustrated by an example where RTSCP is applied to nonlinear model predictive control.
Open AccessReal-Time Sequential Convex Programming for Optimal Control Applications
This paper proposes real-time sequential convex programming (RTSCP), a method for solving a sequence of nonlinear optimization problems depending on an online parameter. We provide a contraction estimate for the proposed method and, as a byproduct, a new proof of the local convergence of sequential convex programming. The approach is illustrated by an example where RTSCP is applied to nonlinear model predictive control.