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Fast Adaptive Tensor Methods Under Local Smoothness
Sadok Jerad
A new, fast adaptive regularization methods is proposed and analyzed under local Lipschitz smoothness of the -th order tensor. For nonconvex problems, it achieves the optimal $\…
A Parameter-Free First-Order Algorithm for Non-Convex Optimization with Global Rate
Sichao Xiong, Sadok Jerad, Coralia Cartis
We introduce PF-AGD, the first parameter-free, deterministic, accelerated first-order method to achieve oracle complexity bound when minimizing sufficiently…
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…
A Fast Newton Method Under Local Lipschitz Smoothness
Serge Gratton, Sadok Jerad, Philippe L. Toint
A new, fast second-order method is proposed that achieves the optimal complexity to obtain first-order -stationary points. Crucially,…
A Stochastic Objective-Function-Free Adaptive Regularization Method with Optimal Complexity
Serge Gratton, Sadok Jerad, Philippe L. Toint
A fully stochastic second-order adaptive-regularization method for unconstrained nonconvex optimization is presented which never computes the objective-function value, but yet achi…
Complexity of Adagrad and other first-order methods for nonconvex optimization problems with bounds constraints
Serge Gratton, Sadok Jerad, Philippe L. Toint
A parametric class of trust-region algorithms for constrained nonconvex optimization is analyzed, where the objective function is never computed. By defining appropriate first-orde…
Yet another fast variant of Newton's method for nonconvex optimization
Serge Gratton, Sadok Jerad, Philippe L. Toint
A class of second-order algorithms is proposed for minimizing smooth nonconvex functions that alternates between regularized Newton and negative curvature steps in an iteration-dep…
Convergence properties of an Objective-Function-Free Optimization regularization algorithm, including an complexity bound
S. Gratton, S. Jerad, Ph. L. Toint
An adaptive regularization algorithm for unconstrained nonconvex optimization is presented in which the objective function is never evaluated, but only derivatives are used. This a…