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Solving Quadratic Programs to High Precision using Scaled Iterative Refinement
[article]
2018
arXiv
pre-print
Quadratic optimization problems (QPs) are ubiquitous, and solution algorithms have matured to a reliable technology. However, the precision of solutions is usually limited due to the underlying floating-point operations. This may cause inconveniences when solutions are used for rigorous reasoning. We contribute on three levels to overcome this issue. First, we present a novel refinement algorithm to solve QPs to arbitrary precision. It iteratively solves refined QPs, assuming a floating-point
arXiv:1803.07178v1
fatcat:bou6l3gtnffzhkt6o7yph4zhia