We propose a novel feasible-path algorithm to solve the optimal power flow (OPF) problem for real-time use cases. The method augments seminal work of Dommel and Tinney with second-order derivatives directly in reduced space induced by equations. In space, optimization includes only inequality constraints corresponding operational constraints. While formulation enforces physical constraints, are...

‎in this paper‎, ‎we propose a feasible interior-point method for‎ ‎convex quadratic programming over symmetric cones‎. ‎the proposed algorithm relaxes the‎ ‎accuracy requirements in the solution of the newton equation system‎, ‎by using an inexact newton direction‎. ‎furthermore‎, ‎we obtain an‎ ‎acceptable level of error in the inexact algorithm on convex‎ ‎quadratic symmetric cone programmin...

We consider a class of constrained optimization problems where the objective function is a sum of a smooth function and a nonconvex non-Lipschitz function. Many problems in sparse portfolio selection, edge preserving image restoration and signal processing can be modelled in this form. First we propose the concept of the Karush-Kuhn-Tucker (KKT) stationary condition for the non-Lipschitz proble...

In this paper we analyze the rate of local convergence of the Newton primal-dual interiorpoint method when the iterates are kept strictly feasible with respect to the inequality constraints. It is shown under the classical conditions that the rate is q–quadratic when the functions associated to the binding inequality constraints are concave. In general, the q–quadratic rate is achieved provided...