Inscribability of a polytope is equivalent to its slack matrix having minimum rank equal to dimension plus one and can be decided by an SDP relaxation of the corresponding rank-minimization problem.
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A hybrid penalty method with pseudo-projection post-processing is introduced for smooth optimization subject to multiple low-rank Hankel matrix constraints, supported by convergence analysis and efficient single-constraint computation via existing SLRA software.
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Determining inscribability of polytopes via rank minimization based on slack matrices
Inscribability of a polytope is equivalent to its slack matrix having minimum rank equal to dimension plus one and can be decided by an SDP relaxation of the corresponding rank-minimization problem.