arXiv:2608. 21359v1 Announce Type: cross Abstract: We develop a new direct accelerated Newton method for minimizing convex functions with Lipschitz continuous Hessian.
By Nikita Doikov
arXiv:2209. 03282v5 Announce Type: replace-cross Abstract: Accelerating the convergence of second-order optimization, particularly Newton-type methods, remains a pivotal challenge in algorithmic research.
By John Chiang
arXiv:2610.33516v1 Announce Type: new
Abstract: Modern gradient boosting algorithms are based on Newton's method, which excels empirically but lacks global convergence guarantees. While recent work o...
By Nikita Zozoulenko, Daniel Falkowski, Thomas Cass, Lukas Gonon
arXiv:2608. 01552v1 Announce Type: cross Abstract: Quadratic Gradient (QG) is a Newton-type optimization framework that bridges first-order gradient descent and second-order optimization by incorporating curvature information into gradient updates.
By John Chiang
arXiv:2608. 12665v1 Announce Type: cross Abstract: For solving nonconvex equality-constrained optimization problems, a recent Gradient-Eigenstep Algorithm by Goyens et al.
By Frank E. Curtis, Lingjun Guo, Daniel P. Robinson
arXiv:2609.08277v1 Announce Type: new
Abstract: We study zeroth-order optimization of non-convex functions with the aid of directional hints, which are cheap but potentially inaccurate approximations...
By Alexander Ryabchenko, Jian Qian, Wenlong Mou