A stable primal---dual approach for linear programming under nondegeneracy assumptions
Computational Optimization and Applications
On Interior-Point Warmstarts for Linear and Combinatorial Optimization
SIAM Journal on Optimization
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Primal-dual interior-point methods for linear complementarity and linear programming problems solve a linear system of equations to obtain a modified Newton step at each iteration. These linear systems become increasingly ill-conditioned in the later stages of the algorithm, but the computed steps are often sufficiently accurate to be useful. We use error analysis techniques tailored to the special structure of these linear systems to explain this observation and examine how theoretically superlinear convergence of a path-following algorithm is affected by the roundoff errors.