The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Dual methods can handle easily complicated constraints in convex problems, but they have typically slow (sublinear) convergence rate in an average primal point, even when the original problem has smooth strongly convex objective function. Primal projected gradient-based methods achieve linear convergence for constrained, smooth and strongly convex optimization, but it is difficult to implement them,...
This paper focuses on the active power loss minimization by optimal voltage control in a power system using a new optimization algorithm. The cost function is assumed to be convex. The algorithm we propose to address the numerical solution of this problem is based on the exploitation of the convex problem structure using a sequential convex programming framework that linearizes the nonlinear power...
In this paper we propose a linear MPC scheme for embedded systems based on the dual fast gradient algorithm for solving the corresponding control problem. We establish computational complexity guarantees for the MPC scheme by appropriately deriving tight convergence estimates of order O(1/k2) for an average primal sequence generated by our proposed numerical optimization algorithm. We also show that...
In this paper we propose an inexact dual gradient method for solving large-scale smooth convex optimization problems. For the proposed algorithm we provide estimates on primal and dual suboptimality and primal infeasibility. We solve the inner problems by means of a parallel coordinate descent method with linear convergence rate. We adapt our method using constraint tightening and obtain a distributed...
In this paper we propose a parallel coordinate descent algorithm for solving smooth convex optimization problems with separable constraints that may arise, e.g. in distributed model predictive control (MPC) for linear network systems. Our algorithm is based on block coordinate descent updates in parallel and has a very simple iteration. We prove (sub)linear rate of convergence for the new algorithm...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.