The Experts below are selected from a list of 1071 Experts worldwide ranked by ideXlab platform
Bart De Schutter - One of the best experts on this subject based on the ideXlab platform.
-
Integration of Resource Allocation Coordination and Branch-and-Bound
2015Co-Authors: Renshi Luo, Romain Bourdais, Ton J.j. Van Den Boom, Bart De SchutterAbstract:In general, integer programming problems are computationally very hard to solve, which makes solving an integer programming problem with a large number of decision variables in a centralized way intractable. In this paper, we propose a novel integer optimization method for strategic planning by integrating the resource allocation coordination method into a branch-and-bound paradigm. Thanks to the distributed computation of the resource allocation coordination method and distributed evaluation of nodes in the branch-and-bound paradigm, our method is capable of solving an integer programming problem in a distributed way. Moreover, since in the branch-and-bound paradigm the size of solution space decreases monotonically as the Iteration Proceeds, it is guaranteed that the globally optimal solution to an integer programming problem is obtained by using our method. Finally, we apply our method to the optimal charging control problem of electric vehicles under constrained grid conditions in a simulation study.
-
CDC - Integration of resource allocation coordination and branch-and-bound
2015 54th IEEE Conference on Decision and Control (CDC), 2015Co-Authors: Renshi Luo, Romain Bourdais, Ton J.j. Van Den Boom, Bart De SchutterAbstract:In general, integer programming problems are computationally very hard to solve, which makes solving an integer programming problem with a large number of decision variables in a centralized way intractable. In this paper, we propose a novel integer optimization method for strategic planning by integrating the resource allocation coordination method into a branch-and-bound paradigm. Thanks to the distributed computation of the resource allocation coordination method and distributed evaluation of nodes in the branch-and-bound paradigm, our method is capable of solving an integer programming problem in a distributed way. Moreover, since in the branch-and-bound paradigm the size of solution space decreases monotonically as the Iteration Proceeds, it is guaranteed that the globally optimal solution to an integer programming problem is obtained by using our method. Finally, we apply our method to the optimal charging control problem of electric vehicles under constrained grid conditions in a simulation study.
Dingguo Pu - One of the best experts on this subject based on the ideXlab platform.
-
Flexible penalty functions for SQP algorithm with additional equality constrained phase
Proceedings of the 2013 International Conference on Advanced Mechatronic Systems, 2013Co-Authors: Bo Wang, Dingguo PuAbstract:A flexible penalty function for sequential quadratic programming(SQP) algorithm is proposed for general nonlinear programming. It is based on the quadratic programming subproblem in which each Iteration Proceeds in two phases. The additional equality constrained phase promotes fast convergence and improves performance in the presence of ill conditioning. The novel feature of the approach is that we employ a flexible penalty function for SQP algorithm with additional equality constrained phase. The flexible penalty function is to promote convergence, where during each Iteration the penalty parameter can be chosen as any number within a prescribed interval, rather than a fixed value. The paper studies the global and local convergence properties of the new algorithm and presents a set of numerical experiments to illustrate its practical performance.
-
A feasible filter SQP algorithm with global and local convergence
Journal of Applied Mathematics and Computing, 2012Co-Authors: Xueqian Li, Dingguo PuAbstract:A feasible sequential quadratic programming (SQP) filter algorithm is proposed for general nonlinear programming. It is based on the modified quadratic programming (QP) subproblem in which each Iteration Proceeds in two phases. The first phase solves a general convex QP problem which does not require any feasibility restoration phase whose computation may be expensive. And, under some mild conditions, the global convergence is proved. The second phase can make the presented SQP method derive quadratic convergence by employing exact Hessian information.
Renshi Luo - One of the best experts on this subject based on the ideXlab platform.
-
Integration of Resource Allocation Coordination and Branch-and-Bound
2015Co-Authors: Renshi Luo, Romain Bourdais, Ton J.j. Van Den Boom, Bart De SchutterAbstract:In general, integer programming problems are computationally very hard to solve, which makes solving an integer programming problem with a large number of decision variables in a centralized way intractable. In this paper, we propose a novel integer optimization method for strategic planning by integrating the resource allocation coordination method into a branch-and-bound paradigm. Thanks to the distributed computation of the resource allocation coordination method and distributed evaluation of nodes in the branch-and-bound paradigm, our method is capable of solving an integer programming problem in a distributed way. Moreover, since in the branch-and-bound paradigm the size of solution space decreases monotonically as the Iteration Proceeds, it is guaranteed that the globally optimal solution to an integer programming problem is obtained by using our method. Finally, we apply our method to the optimal charging control problem of electric vehicles under constrained grid conditions in a simulation study.
-
CDC - Integration of resource allocation coordination and branch-and-bound
2015 54th IEEE Conference on Decision and Control (CDC), 2015Co-Authors: Renshi Luo, Romain Bourdais, Ton J.j. Van Den Boom, Bart De SchutterAbstract:In general, integer programming problems are computationally very hard to solve, which makes solving an integer programming problem with a large number of decision variables in a centralized way intractable. In this paper, we propose a novel integer optimization method for strategic planning by integrating the resource allocation coordination method into a branch-and-bound paradigm. Thanks to the distributed computation of the resource allocation coordination method and distributed evaluation of nodes in the branch-and-bound paradigm, our method is capable of solving an integer programming problem in a distributed way. Moreover, since in the branch-and-bound paradigm the size of solution space decreases monotonically as the Iteration Proceeds, it is guaranteed that the globally optimal solution to an integer programming problem is obtained by using our method. Finally, we apply our method to the optimal charging control problem of electric vehicles under constrained grid conditions in a simulation study.
Romain Bourdais - One of the best experts on this subject based on the ideXlab platform.
-
Integration of Resource Allocation Coordination and Branch-and-Bound
2015Co-Authors: Renshi Luo, Romain Bourdais, Ton J.j. Van Den Boom, Bart De SchutterAbstract:In general, integer programming problems are computationally very hard to solve, which makes solving an integer programming problem with a large number of decision variables in a centralized way intractable. In this paper, we propose a novel integer optimization method for strategic planning by integrating the resource allocation coordination method into a branch-and-bound paradigm. Thanks to the distributed computation of the resource allocation coordination method and distributed evaluation of nodes in the branch-and-bound paradigm, our method is capable of solving an integer programming problem in a distributed way. Moreover, since in the branch-and-bound paradigm the size of solution space decreases monotonically as the Iteration Proceeds, it is guaranteed that the globally optimal solution to an integer programming problem is obtained by using our method. Finally, we apply our method to the optimal charging control problem of electric vehicles under constrained grid conditions in a simulation study.
-
CDC - Integration of resource allocation coordination and branch-and-bound
2015 54th IEEE Conference on Decision and Control (CDC), 2015Co-Authors: Renshi Luo, Romain Bourdais, Ton J.j. Van Den Boom, Bart De SchutterAbstract:In general, integer programming problems are computationally very hard to solve, which makes solving an integer programming problem with a large number of decision variables in a centralized way intractable. In this paper, we propose a novel integer optimization method for strategic planning by integrating the resource allocation coordination method into a branch-and-bound paradigm. Thanks to the distributed computation of the resource allocation coordination method and distributed evaluation of nodes in the branch-and-bound paradigm, our method is capable of solving an integer programming problem in a distributed way. Moreover, since in the branch-and-bound paradigm the size of solution space decreases monotonically as the Iteration Proceeds, it is guaranteed that the globally optimal solution to an integer programming problem is obtained by using our method. Finally, we apply our method to the optimal charging control problem of electric vehicles under constrained grid conditions in a simulation study.
Ton J.j. Van Den Boom - One of the best experts on this subject based on the ideXlab platform.
-
Integration of Resource Allocation Coordination and Branch-and-Bound
2015Co-Authors: Renshi Luo, Romain Bourdais, Ton J.j. Van Den Boom, Bart De SchutterAbstract:In general, integer programming problems are computationally very hard to solve, which makes solving an integer programming problem with a large number of decision variables in a centralized way intractable. In this paper, we propose a novel integer optimization method for strategic planning by integrating the resource allocation coordination method into a branch-and-bound paradigm. Thanks to the distributed computation of the resource allocation coordination method and distributed evaluation of nodes in the branch-and-bound paradigm, our method is capable of solving an integer programming problem in a distributed way. Moreover, since in the branch-and-bound paradigm the size of solution space decreases monotonically as the Iteration Proceeds, it is guaranteed that the globally optimal solution to an integer programming problem is obtained by using our method. Finally, we apply our method to the optimal charging control problem of electric vehicles under constrained grid conditions in a simulation study.
-
CDC - Integration of resource allocation coordination and branch-and-bound
2015 54th IEEE Conference on Decision and Control (CDC), 2015Co-Authors: Renshi Luo, Romain Bourdais, Ton J.j. Van Den Boom, Bart De SchutterAbstract:In general, integer programming problems are computationally very hard to solve, which makes solving an integer programming problem with a large number of decision variables in a centralized way intractable. In this paper, we propose a novel integer optimization method for strategic planning by integrating the resource allocation coordination method into a branch-and-bound paradigm. Thanks to the distributed computation of the resource allocation coordination method and distributed evaluation of nodes in the branch-and-bound paradigm, our method is capable of solving an integer programming problem in a distributed way. Moreover, since in the branch-and-bound paradigm the size of solution space decreases monotonically as the Iteration Proceeds, it is guaranteed that the globally optimal solution to an integer programming problem is obtained by using our method. Finally, we apply our method to the optimal charging control problem of electric vehicles under constrained grid conditions in a simulation study.
