3 citations · 5 across the 4 of their papers we have counts for
4 papers
Local Convergence of Adaptively Regularized Tensor Methods
Karl Welzel, Yang Liu, Raphael A. Hauser +1
Optimization methods that make use of derivatives of the objective function up to order are called tensor methods. Among them, ones that minimize a regularized th-order…
On Global Rates for Regularization Methods based on Secant Derivative Approximations
Coralia Cartis, Sadok Jerad, Karl Welzel
An inexact and globally convergent framework for high-order adaptive regularization methods is presented, in which approximations may be used for the th-order tensor, based on l…
Efficient Implementation of Third-Order Tensor Methods with Adaptive Regularization for Unconstrained Optimization
Coralia Cartis, Raphael Hauser, Yang Liu +2
High-order tensor methods that employ local Taylor models of degree within adaptive regularization frameworks (AR) have recently received significant attention, due to their…
Approximating Higher-Order Derivative Tensors Using Secant Updates
Karl Welzel, Raphael A. Hauser
Quasi-Newton methods employ an update rule that gradually improves the Hessian approximation using the already available gradient evaluations. We propose higher-order secant update…