Mirror descent and nonlinear projected subgradient methods for convex optimization
A Beck, M Teboulle - Operations Research Letters, 2003 - Elsevier
The mirror descent algorithm (MDA) was introduced by Nemirovsky and Yudin for solving
convex optimization problems. This method exhibits an efficiency estimate that is mildly
dependent in the decision variables dimension, and thus suitable for solving very large
scale optimization problems. We present a new derivation and analysis of this algorithm. We
show that the MDA can be viewed as a nonlinear projected-subgradient type method,
derived from using a general distance-like function instead of the usual Euclidean squared …
convex optimization problems. This method exhibits an efficiency estimate that is mildly
dependent in the decision variables dimension, and thus suitable for solving very large
scale optimization problems. We present a new derivation and analysis of this algorithm. We
show that the MDA can be viewed as a nonlinear projected-subgradient type method,
derived from using a general distance-like function instead of the usual Euclidean squared …