# Comparing different subgradient methods for solving convex optimization problems with functional constraints

arXiv: https://arxiv.org/abs/2101.01045 · HTML: https://mlsanity.com/paper/2101.01045/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 13 references that are in the map.
- First posted on arXiv 4 Jan 2021.

## Builds on (most cited first, 1 of 1)

- [Primal-dual subgradient method for constrained convex optimization problems](https://mlsanity.com/paper/2009.12769/) (2020)

## Abstract (arXiv)

We consider the problem of minimizing a convex, nonsmooth function subject to a closed convex constraint domain. The methods that we propose are reforms of subgradient methods based on Metel--Takeda's paper [Optimization Letters 15.4 (2021): 1491-1504] and Boyd's works [Lecture notes of EE364b, Stanford University, Spring 2013-14, pp. 1-39]. While the former has complexity O(varepsilon-2r) for all r> 1, the complexity of the latter is O(varepsilon-2). We perform some comparisons between these two methods using several test examples.