# Sequential Randomized Algorithms for Convex Optimization in the Presence of Uncertainty

arXiv: https://arxiv.org/abs/1304.2222 · HTML: https://mlsanity.com/paper/1304.2222/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2013.

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

- [A Sequential Deep Learning Algorithm for Sampled Mixed-integer Optimisation Problems](https://mlsanity.com/paper/2301.10703/) (2023)

## Abstract (arXiv)

In this paper, we propose new sequential randomized algorithms for convex optimization problems in the presence of uncertainty. A rigorous analysis of the theoretical properties of the solutions obtained by these algorithms, for full constraint satisfaction and partial constraint satisfaction, respectively, is given. The proposed methods allow to enlarge the applicability of the existing randomized methods to real-world applications involving a large number of design variables. Since the proposed approach does not provide a priori bounds on the sample complexity, extensive numerical simulations, dealing with an application to hard-disk drive servo design, are provided. These simulations testify the goodness of the proposed solution.