2022, Volume 14, Issue 2

Special issue

	Editor’s note A. V. Gasnikov, A. I. Lobanov		209–212
MATHEMATICAL MODELING AND NUMERICAL SIMULATION
	Lower bounds for conditional gradient type methods for minimizing smooth strongly convex functions A. D. Agafonov		213–223
	An approach for the nonconvex uniformly concave structured saddle point problem M. S. Alkousa, A. V. Gasnikov, P. E. Dvurechenskii, A. A. Sadiev, L. Ya. Razouk		225–237
	Linearly convergent gradient-free methods for minimization of parabolic approximation A. I. Bazarova, A. N. Beznosikov, A. V. Gasnikov		239–255
	Variance reduction for minimax problems with a small dimension of one of the variables E. L. Gladin, E. D. Borodich		257–275
	Averaged heavy-ball method M. Yu. Danilova, G. Malinovskii		277–308
	On the relations of stochastic convex optimization problems with empirical risk minimization problems on p-norm balls D. M. Dvinskikh, V. V. Pirau, A. V. Gasnikov		309–319
	A gradient method with inexact oracle for composite nonconvex optimization P. E. Dvurechenskii		321–334
	Proof of the connection between the Backman model with degenerate cost functions and the model of stable dynamics E. V. Kotlyarova, K. Yu. Krivosheev, E. V. Gasnikova, Yu. I. Sharovatova, A. V. Shurupov		335–342
	Speeding up the two-stage simultaneous traffic assignment model E. V. Kotlyarova, P. A. Severilov, Ya. P. Ivchenkov, P. V. Mokrov, M. O. Chekanov, E. V. Gasnikova, Yu. I. Sharovatova		343–355
	Tensor methods for strongly convex strongly concave saddle point problems and strongly monotone variational inequalities P. A. Ostroukhov, R. A. Kamalov, P. E. Dvurechenskii, A. V. Gasnikov		357–376
	Tensor methods inside mixed oracle for min-min problems P. A. Ostroukhov		377–398
	Stochastic optimization in digital pre-distortion of the signal A. V. Alpatov, E. A. Peters, D. A. Pasechnyuk, A. M. Raigorodskii		399–416
	Application of gradient optimization methods to solve the Cauchy problem for the Helmholtz equation N. V. Pletnev, P. E. Dvurechenskii, A. V. Gasnikov		417–444
	Adaptive first-order methods for relatively strongly convex optimization problems O. S. Savchuk, A. A. Titov, F. S. Stonyakin, M. S. Alkousa		445–472
	Subgradient methods for non-smooth optimization problems with some relaxation of sharp minimum S. S. Ablaev, D. V. Makarenko, F. S. Stonyakin, M. S. Alkousa, I. V. Baran		473–495
	On accelerated adaptive methods and their modifications for alternating minimization N. K. Tupitsa		497–515