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A Damped Newton Method Achieves Global and Local Quadratic Convergence Rate
Slavomír Hanzely, Dmitry Kamzolov, Dmitry Pasechnyuk +3
In this paper, we present the first stepsize schedule for Newton method resulting in fast global and local convergence guarantees. In particular, a) we prove an $O\left( \frac 1 {k…
Minibatch Stochastic Three Points Method for Unconstrained Smooth Minimization
Soumia Boucherouite, Grigory Malinovsky, Peter Richtárik +1
In this paper, we propose a new zero order optimization method called minibatch stochastic three points (MiSTP) method to solve an unconstrained minimization problem in a setting w…
RandProx: Primal-Dual Optimization Algorithms with Randomized Proximal Updates
Laurent Condat, Peter Richtárik
Proximal splitting algorithms are well suited to solving large-scale nonsmooth optimization problems, in particular those arising in machine learning. We propose a new primal-dual…
A Note on the Convergence of Mirrored Stein Variational Gradient Descent under Smoothness Condition
Lukang Sun, Peter Richtárik
In this note, we establish a descent lemma for the population limit Mirrored Stein Variational Gradient Method~(MSVGD). This descent lemma does not rely on the path information of…
Optimal Algorithms for Decentralized Stochastic Variational Inequalities
Dmitry Kovalev, Aleksandr Beznosikov, Abdurakhmon Sadiev +3
Variational inequalities are a formalism that includes games, minimization, saddle point, and equilibrium problems as special cases. Methods for variational inequalities are theref…