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In this paper, we present the implementation of infeasible kernel-based primal-dual interior-point methods for linearly constrained convex optimization. Numerical results are provided to demonstrate the efficiency of the algorithms.

Implementation of Infeasible Kernel-Based Interior-Point Methods for Linearly Constrained Convex Optimization