Warmstarting for interior point methods applied to the long-term power planning problem

A New Unblocking Technique to Warmstart Interior Point Methods Based on Sensitivity Analysis

One of the main drawbacks associated with Interior Point Methods (IPM) is the perceived lack of an efficient warmstarting scheme which would enable the use of information from a previous solution of a similar problem. Recently there has been renewed interest in the subject. A common problem with warmstarting for IPM is that an advanced starting point which is close to the boundary of the feasib...

- generation and interior point methods applied to the long - term electric power - planning problem PhD Student : Adela Pagès Directed by : Prof

Warmstarting the homogeneous and self-dual interior point method for linear and conic quadratic problems

We present two strategies for warmstarting primal-dual interior point methods for the homogeneous self-dual model when applied to mixed linear and quadratic conic optimization problems. Common to both strategies is their use of only the final (optimal) iterate of the initial problem and their negligible computational cost. This is a major advantage when compared to previously suggested strategi...

A Primal-Dual Slack Approach to Warmstarting Interior-Point Methods for Linear Programming

Despite the many advantages of interior-point algorithms over active-set methods for linear programming, one of their practical limitations is the remaining challenge to efficiently solve several related problems by an effective warmstarting strategy. Similar to earlier approaches that modify the initial problem by shifting the boundary of its feasible region, the contribution of this paper is ...

An infeasible interior-point method for the $P*$-matrix linear complementarity problem based on a trigonometric kernel function with full-Newton step

An infeasible interior-point algorithm for solving the$P_*$-matrix linear complementarity problem based on a kernelfunction with trigonometric barrier term is analyzed. Each (main)iteration of the algorithm consists of a feasibility step andseveral centrality steps, whose feasibility step is induced by atrigonometric kernel function. The complexity result coincides withthe best result for infea...