5 papers · 1 filter
Adaptive Regularized Newton Method with Inexact Hessian
Aleksandr Shestakov, Nail Bashirov, Andrei Semenov +4
Newton's method is the most widespread high-order method, demanding the gradient and the Hessian of the objective function. However, one of the main disadvantages of Newtons method…
Gradient-Normalized Smoothness for Optimization with Approximate Hessians
Andrei Semenov, Martin Jaggi, Nikita Doikov
In this work, we develop new optimization algorithms that use approximate second-order information combined with the gradient regularization technique to achieve fast global conver…
Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under -Smoothness
Nikita Kornilov, Philip Zmushko, Andrei Semenov +3
In recent years, non-convex optimization problems are more often described by generalized -smoothness assumption rather than standard one. Meanwhile, severely corrupted…
Mixed Newton Method for Optimization in Complex Spaces
Nikita Yudin, Roland Hildebrand, Sergey Bakhurin +5
In this paper, we modify and apply the recently introduced Mixed Newton Method, which is originally designed for minimizing real-valued functions of complex variables, to the minim…
Bregman Proximal Method for Efficient Communications under Similarity
Aleksandr Beznosikov, Darina Dvinskikh, Dmitry Bylinkin +2
We propose a novel stochastic distributed method for both monotone and strongly monotone variational inequalities with Lipschitz operator and proper convex regularizers arising in…