Paper Info
Title
An Optimal Algorithm for Strongly Convex Minimization under Affine Constraints
Abstract
Optimization problems under affine constraints appear in various areas of machine learning. We consider the task of minimizing a smooth strongly convex function F(x) under the affine constraint Kx = b, with an oracle providing evaluations of the gradient of F and multiplications by K and its transpose. We provide lower bounds on the number of gradient computations and matrix multiplications to achieve a given accuracy. Then we propose an accelerated primal-dual algorithm achieving these lower bounds. Our algorithm is the first optimal algorithm for this class of problems.
Year
Venue
DocType
2022
INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 151
Conference
Volume
ISSN
Citations 
151
2640-3498
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Adil Salim124.42
Laurent Condat2656.92
Kovalev, Dmitry314.40
Peter Richtárik4131484.53