4 papers
Wall-Clock Complexity for Zeroth-Order Optimization with Tunable Oracle Fidelity
Alexandra Suvorikova, Igor Pavlov, Artem Vasin +4
Zeroth-order (black-box) optimization is applied when gradients are unavailable and objective evaluations rely on expensive simulations. In many such applications, the oracle fidel…
On Solving Minimization and Min-Max Problems by First-Order Methods with Relative Error in Gradients
Artem Vasin, Valery Krivchenko, Dmitry Kovalev +6
First-order methods for minimization and saddle point (min-max) problems are widely used for solving large-scale problems, in particular arising in machine learning. The majority o…
Gradient directions and relative inexactness in optimization and machine learning
Artem Vasin
In this paper, we investigate the influence of noise giving an estimate of the gradient having a acute angle with the original. Noise amplitude has a relative model. The work offer…
Accelerated gradient methods with absolute and relative noise in the gradient
Vasin Artem, Alexander Gasnikov, Pavel Dvurechensky +1
In this paper, we investigate accelerated first-order methods for smooth convex optimization problems under inexact information on the gradient of the objective. The noise in the g…