HALO-LP: A Homogeneous All-Shifted Non-Interior-Point Method for Linear Programming
Overview
This paper introduces HALO-LP, a linear programming method combining homogeneous self-dual embedding with all-shifted NCP functions and proximal regularization. It enables arbitrary primal-dual warm starts without strictly interior iterates and detects infeasibility via Farkas certificates. Numerical tests on Netlib instances show state-of-the-art warm-starting performance, while applications to quadrupedal robot optimal control are demonstrated.
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- arXiv Robotics — research abstractsHALO-LP: A Homogeneous All-Shifted Non-Interior-Point Method for Linear Programming
This paper introduces HALO-LP, a linear programming method combining homogeneous self-dual embedding with all-shifted NCP functions and proximal regularization. It enables arbitrary primal-dual warm starts without strictly interior iterates and detects infeasibility via Farkas certificates. Numerical tests on Netlib instances show state-of-the-art warm-starting performance, while applications to quadrupedal robot optimal control are demonstrated.
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