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Shafiu Jibrin(shafiujyahoo.com) Abstract: We study the problem of computing weighted analytic center for system of linear matrix inequality constraints. The problem can be solved using the Standard Newton's method. However, this approach requires that a starting point in the interior point of the feasible region be given or a Phase I problem be solved. We address the problem by using infeasible Newton's method applied to the KKT system of equations which can be started from any point. We implement the method using backtracking line search technique and also study the effect of large weights on the method. We use numerical experiments to compare infeasible Newton's method with the Standard Newton's method. The results show that infeasible Newton's method moves in the interior of the feasible regions often very quickly, starting from any point. We recommend it as a method for finding an interior point by setting each weight to be 1. It appears to work better than the Standard Newton's method in finding the weighted analytic center when none of weights is very large relative to the other weights. However, we find that infeasible Newton's method is more sensitive than the Standard Newton's method to large variation in the weights. Keywords: Linear Matrix Inequalities, Semidefinite Programming, Analytic Center Category 1: Linear, Cone and Semidefinite Programming (Semidefinite Programming ) Category 2: Convex and Nonsmooth Optimization (Convex Optimization ) Category 3: Nonlinear Optimization (Constrained Nonlinear Optimization ) Citation: Journal of Mathematics, vol. 2015, Article ID 456392, 9 pages, 2015 Download: [PDF] Entry Submitted: 02/12/2017 Modify/Update this entry  
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