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We present an inexact interior point proximal method to solve
linearly constrained convex problems. In fact, we derive a
primal-dual algorithm to solve the KKT conditions of the
optimization problem using a modified version of the rescaled
proximal method. We also present a pure primal method.
The proposed proximal method has as distinctive feature the
possibility of allowing inexact inner steps even for Linear
Programming. This is achieved by using an error criterion that
bounds the subgradient...
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