SOC-ICNNs admit exact dual-variable recovery of first-order geometry and local Hessians as value functions of SOCPs.
Learning parametric convex functions.arXiv preprint arXiv:2506.04183, June
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
SOC-ICNN generalizes ReLU-based ICNNs to SOCP, strictly expanding the class of representable convex functions while preserving similar forward-pass complexity.
An active-learning method fits nonlinear surrogates by minimizing maximum approximation error and derives worst-case error bounds over the domain.
A surrogate for parametric nonconvex optimization is constructed as the minimum of convex-monotonic function compositions and solved via parallel convex optimization, with a proof-of-concept on path tracking.
citing papers explorer
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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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SOC-ICNN: From Polyhedral to Conic Geometry for Learning Convex Surrogate Functions
SOC-ICNN generalizes ReLU-based ICNNs to SOCP, strictly expanding the class of representable convex functions while preserving similar forward-pass complexity.
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Worst-case Nonlinear Regression with Error Bounds
An active-learning method fits nonlinear surrogates by minimizing maximum approximation error and derives worst-case error bounds over the domain.
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Parametric Nonconvex Optimization via Convex Surrogates
A surrogate for parametric nonconvex optimization is constructed as the minimum of convex-monotonic function compositions and solved via parallel convex optimization, with a proof-of-concept on path tracking.