SOC-ICNNs admit exact dual-variable recovery of first-order geometry and local Hessians as value functions of SOCPs.
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Full-batch gradient descent forces the largest Hessian eigenvalue to exactly 2/η via the edge coupling functional, its criticality condition, and the mean value theorem with no gap.
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Exact Dual Geometry of SOC-ICNN Value Functions
SOC-ICNNs admit exact dual-variable recovery of first-order geometry and local Hessians as value functions of SOCPs.
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The Origin of Edge of Stability
Full-batch gradient descent forces the largest Hessian eigenvalue to exactly 2/η via the edge coupling functional, its criticality condition, and the mean value theorem with no gap.
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