5 papers
Decentralized Inexact Cubic Newton Method with Consensus Procedure
Artem Agafonov, Anton Novitskii, Alexander Rogozin +5
Distributed optimization is widely used in large-scale and privacy-preserving machine learning, where each agent stores a local objective and communicates only with its neighbors i…
Cubic Regularized Newton Method with Variance Reduction for Finite-sum Non-convex Problems
Dmitry Pasechnyuk-Vilensky, Dmitry Kamzolov, Martin TakáÄ
We study finite-sum non-convex optimization and analyze a variance-reduced cubic Newton method based on EMA-smoo…
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…
DAG: Projected Stochastic Approximation Iteration for DAG Structure Learning
Klea Ziu, SlavomÃr Hanzely, Loka Li +3
Learning the structure of Directed Acyclic Graphs (DAGs) presents a significant challenge due to the vast combinatorial search space of possible graphs, which scales exponentially…
OPTAMI: Global Superlinear Convergence of High-order Methods
Dmitry Kamzolov, Dmitry Pasechnyuk, Artem Agafonov +2
Second-order methods for convex optimization outperform first-order methods in terms of theoretical iteration convergence, achieving rates up to for highly-smooth funct…