Gradient descent, Fast Gradient Descent, Function Model, Universal Gradient Method, Conjugate Gradient Method, Composite Optimization
Subject:
Optimization methods
Main publications:
Anton Anikin, Alexander Gasnikov, Pavel Dvurechensky, Alexander Turin, Alexey Chernov, “Dual Approaches to the Minimization of Strongly Convex
Functionals with a Simple Structure under Affine Constraints”, Computational Mathematics and Mathematical Physics, 57 (2017)
A. I. Turin, “Primal-dual fast gradient method with a model”, Computer Research and Modeling, 12:2 (2020), 263–274
2.
D. M. Dvinskikh, S. S. Omelchenko, A. V. Gasnikov, A. I. Turin, “Accelerated gradient sliding for minimizing a sum of functions”, Dokl. RAN. Math. Inf. Proc. Upr., 492 (2020), 85–88; Dokl. Math., 101:3 (2020), 244–246
D. M. Dvinskikh, A. I. Turin, A. V. Gasnikov, S. S. Omelchenko, “Accelerated and Unaccelerated Stochastic Gradient Descent in Model Generality”, Mat. Zametki, 108:4 (2020), 515–528; Math. Notes, 108:4 (2020), 511–522
A. V. Gasnikov, A. I. Turin, “Fast gradient descent for convex minimization problems with an oracle producing a $(\delta,L)$-model of function at the requested point”, Zh. Vychisl. Mat. Mat. Fiz., 59:7 (2019), 1137–1150; Comput. Math. Math. Phys., 59:7 (2019), 1085–1097
A. S. Anikin, A. V. Gasnikov, P. E. Dvurechensky, A. I. Tyurin, A. V. Chernov, “Dual approaches to the minimization of strongly convex functionals with a simple structure under affine constraints”, Zh. Vychisl. Mat. Mat. Fiz., 57:8 (2017), 1270–1284; Comput. Math. Math. Phys., 57:8 (2017), 1262–1276
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