Polynomial-time interior-point algorithm based on a local self-concordant finite barrier function
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Abstract
The choice of self-concordant functions is the key to efficient algorithms for linear and quadratic convex optimizations, which provide a method with polynomial-time iterations to solve linear and quadratic convex optimization problems. The parameters of a self-concordant barrier function can be used to compute the complexity bound of the proposed algorithm. In this paper, it is proved that the finite barrier function is a local self-concordant barrier function. By deriving the local values of parameters of this barrier function, the desired complexity bound of an interior-point algorithm based on this local self-concordant function for linear optimization problem is obtained. The bound matches the best known bound for small-update methods.
Keywords
linear optimization self-concordant function finite barrier interior-point methods polynomial-time complexity2000 Mathematics Subject Classification
90C25 90C51Preview
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