A HYBRID ADAPTIVE ALGORITHM FOR LINEAR OPTIMIZATION

Recently, using the framework of self-regularity, Salahi in his Ph.D. thesis proposed an adaptive single step algorithm which takes advantage of the current iterate information to find an appropriate barrier parameter rather than using a fixed fraction of the current duality gap. However, his algorithm might do at most one bad step after each good step in order to keep the iterate in a certain neighborhood of the central path. In this paper, using the same framework, we propose a hybrid adaptive algorithm. Depending on the position of the current iterate, our new algorithm uses either the classical Newton search direction or a self-regular search direction. The larger the distance from the central path, the larger the barrier degree of the self-regular search direction is. Unlike the classical approach, here we control the iterates by guaranteeing certain reduction of the proximity measure. This itself leads to a one dimensional equation which determines the target barrier parameter at each iteration. This allows us to have a large update algorithm without any need for safeguard or special steps. Finally, we prove that our hybrid adaptive algorithm has an $\mathcal{O}\Big(n^\frac{71}{100}\, {\rm log}\, n\, {\rm log}\, \frac{(x^0)^Ts^0}{\epsilon}\Big)$ worst case iteration complexity.