
doi: 10.1007/bf02614377
We provide a survey of interior-point methods for linear programming and its extensions that are based on reducing a suitable potential function at each iteration. We give a fairly complete overview of potential-reduction methods for linear programming, focusing on the possibility of taking long steps and the properties of the barrier function that are necessary for the analysis. We then describe briefly how the methods and results can be extended to certain convex programming problems, following the approach of Nesterov and Todd. We conclude with some open problems.
potential-reduction methods, Interior-point methods, Self-scaled barriers, self-concordant barriers, self-scaled barriers, Potential functions, interior-point methods, Linear programming, survey, Self-concordant barriers
potential-reduction methods, Interior-point methods, Self-scaled barriers, self-concordant barriers, self-scaled barriers, Potential functions, interior-point methods, Linear programming, survey, Self-concordant barriers
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