Convex optimization in Hilbert space with applications to inverse problems

Abstract

In this paper we propose accelerated gradient descent schemes for convex optimization problems in Hilbert space. We consider inexact oracle case.

20 pages. Submitted to Applied Numerical Mathematics

References in corpus (3)

• Universal fast gradient method for stochastic composit optimization problems
• Mirror version of similar triangles method for constrained optimization problems
• Dual approaches to the strongly convex simple function minimization problem under affine restrictions

Cited by in corpus (2)

• Optimal Algorithms for Distributed Optimization
• About some works of Boris Polyak on convergence of gradient methods and their development