English, Article edition: A HYBRID ADAPTIVE ALGORITHM FOR LINEAR OPTIMIZATION MAZIAR SALAHI; TAMÃS TERLAKY

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Bookmark: http://trove.nla.gov.au/version/40226
Physical Description
• article
Language
• English

Edition details

Title
• A HYBRID ADAPTIVE ALGORITHM FOR LINEAR OPTIMIZATION
Author
• MAZIAR SALAHI
• TAMÃS TERLAKY
Physical Description
• article
Notes
• 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.
• Linear optimization, interior point methods, self-regular functions, polynomial complexity
• RePEc:wsi:apjorx:v:26:y:2009:i:02:p:235-256
Language
• English
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