Two new state-lifting moment-SOS hierarchies exploit composition and tensor train structure to compute certified bounds for large polynomial optimization problems.
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Neural networks minimize Willmore energy on embedded surfaces, recovering the round sphere and Clifford torus while supplying a search procedure for genus-2 minimal surfaces.
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Composition and tensor train structure in polynomial optimization
Two new state-lifting moment-SOS hierarchies exploit composition and tensor train structure to compute certified bounds for large polynomial optimization problems.
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Minimising Willmore Energy via Neural Flow
Neural networks minimize Willmore energy on embedded surfaces, recovering the round sphere and Clifford torus while supplying a search procedure for genus-2 minimal surfaces.