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Abstract
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In this paper, we present a theoretical framework of an interior-point method for solving
linear optimization problems over symmetric cones. First, we de ne a new neighborhood of the central
path and show that the de ned neighborhood is wider than the neighborhoods that are available. Then,
the convergence of the algorithm is investigated and, using an elegant analysis and Euclidean Jordan
algebra as a tool is shown that the iteration complexity coincides with the best-known one obtained
by any feasible interior-point method that uses the Nesterov-Todd direction. Finally, numerical results
show that the proposed algorithm is efficient and promising.
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