arXiv AI
Dual Certified White-Box Inference for Input Convex Neural Networks
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Affine-invariant Cubic Newton with Weak Learners
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LEAF: A Learning-Enabled ADMM Framework for Accelerated Convex Optimization
arXiv:2606. 08993v1 Announce Type: new Abstract: We propose LEAF, a learning-enabled ADMM framework for accelerated convex optimization.
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Simplified Quadratic Gradient: A Unified Framework Bridging Gradient Descent and Newton-Type Methods by Synthesizing Hessians and Gradients
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Generalized Convexity and Smoothness via Conjugate Duality: Optimization Theory for Deep Neural Networks
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Input convex neural networks as surrogates in mathematical optimisation
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Hugging Face Trending Papers
Oct 19
Learning SOCP Solver Dynamics with Graph Neural Networks
Second-order cone programs (SOCPs) are a fundamental class of convex optimization problems widely applied across engineering domains. Classical interior-point methods are robust and accurate, but thei...