Web: http://arxiv.org/abs/2112.07344

June 17, 2022, 1:11 a.m. | Adeyemi D. Adeoye, Alberto Bemporad

cs.LG updates on arXiv.org arxiv.org

In this paper, we propose the SCORE (self-concordant regularization)
framework for unconstrained minimization problems which incorporates
second-order information in the Newton decrement framework for convex
optimization. We propose the generalized Gauss-Newton with Self-Concordant
Regularization (GGN-SCORE) algorithm that updates the minimization variables
each time it receives a new input batch. The proposed algorithm exploits the
structure of the second-order information in the Hessian matrix, thereby
reducing computational overhead. GGN-SCORE demonstrates how we may speed up
convergence while also improving model generalization …

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