# Convergence and rate of convergence of some greedy algorithms in convex optimization

arXiv: https://arxiv.org/abs/1412.3297 · HTML: https://mlsanity.com/paper/1412.3297/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2014.

## Built on by (most cited first, 1 of 1)

- [Risk Bounds for Mixture Density Estimation on Compact Domains via the h-Lifted Kullback--Leibler Divergence](https://mlsanity.com/paper/2404.12586/) (2024)

## Abstract (arXiv)

The paper gives a systematic study of the approximate versions of three greedy-type algorithms that are widely used in convex optimization. By approximate version we mean the one where some of evaluations are made with an error. Importance of such versions of greedy-type algorithms in convex optimization and in approximation theory was emphasized in previous literature.