A SELF-REGULAR NEWTON BASED ALGORITHM FOR LINEAR OPTIMIZATION
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AbstractIn this paper, using the framework of self-regularity, we propose a hybrid adaptive algorithm for the linear optimization problem. If the current iterates are far from a central path, the algorithm employs a self-regular search direction, otherwise the classical Newton search direction is employed. This feature of the algorithm allows us to prove a worst case iteration bound. Our result matches the best iteration bound obtained by the pure self-regular approach and improves on the worst case iteration bound of the classical algorithm.
2009 ◽
Vol 26
(02)
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pp. 235-256
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2011 ◽
Vol 01
(04)
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pp. 294-302
1987 ◽
Vol 23
(2)
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pp. 107-116
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1991 ◽
Vol 21
(6-7)
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pp. 77-85
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2018 ◽