Anders Skajaa

A homogeneous interior-point algorithm for nonsymmetric convex conic optimization

A homogeneous interior-point algorithm for solving nonsymmetric convex conic optimization problems is presented. Starting each iteration from the vicinity of the central path, the method steps in the approximate tangent direction and then applies a correction phase to locate the next well-centered primal–dual point. Features of the algorithm include that it makes use only of the primal barrier function, that it is able to detect infeasibilities in the problem and that no phase-I method is needed. We prove convergence to

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

\epsilon

A riccati based homogeneous and self-dual interior-point method for linear economic model predictive control

ϵ-accuracy in

A Homogeneous and Self-Dual Interior-Point Linear Programming Algorithm for Economic Model Predictive Control

{\mathcal {O}}(\sqrt{\nu } \log {(1/\epsilon )})

On Implementing a Homogeneous Interior-Point Algorithm for Nonsymmetric Conic Optimization

O(νlog(1/ϵ)) iterations. To improve performance, the algorithm employs a new Runge–Kutta type second order search direction suitable for the general nonsymmetric conic problem. Moreover, quasi-Newton updating is used to reduce the number of factorizations needed, implemented so that data sparsity can still be exploited. Extensive and promising computational results are presented for the