# An Efficient Interior-Point Method for Online Convex Optimization

arXiv: https://arxiv.org/abs/2307.11668 · HTML: https://mlsanity.com/paper/2307.11668/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 19 references that are in the map.
- First posted on arXiv 21 Jul 2023.

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

- [A Simple, Optimal and Efficient Algorithm for Online Exp-Concave Optimization](https://mlsanity.com/paper/2512.23190/) (2025)

## Abstract (arXiv)

A new algorithm for regret minimization in online convex optimization is described. The regret of the algorithm after T time periods is O(√T log T) - which is the minimum possible up to a logarithmic term. In addition, the new algorithm is adaptive, in the sense that the regret bounds hold not only for the time periods 1,ldots,T but also for every sub-interval s,s+1,ldots,t. The running time of the algorithm matches that of newly introduced interior point algorithms for regret minimization: in n-dimensional space, during each iteration the new algorithm essentially solves a system of linear equations of order n, rather than solving some constrained convex optimization problem in n dimensions and possibly many constraints.