# A Stochastic Conjugate Subgradient Algorithm for Two-stage Stochastic Programming

arXiv: https://arxiv.org/abs/2503.21053 · HTML: https://mlsanity.com/paper/2503.21053/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2025.

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

- [Beyond First-Order: Training LLMs with Stochastic Conjugate Subgradients and AdamW](https://mlsanity.com/paper/2507.01241/) (2025)

## Abstract (arXiv)

Stochastic Optimization is a cornerstone of operations research, providing a framework to solve optimization problems under uncertainty. Despite the development of numerous algorithms to tackle these problems, several persistent challenges remain, including: (i) selecting an appropriate sample size, (ii) determining an effective search direction, and (iii) choosing a proper step size. This paper introduces a comprehensive framework, the Stochastic Conjugate Subgradient (SCS) framework, designed to systematically address these challenges. Specifically, The SCS framework offers structured approaches to determining the sample size, the search direction, and the step size. By integrating various stochastic algorithms within the SCS framework, we have developed a novel stochastic algorithm for two-stage stochastic programming. The convergence and convergence rates of the algorithm have been rigorously established, with preliminary computational results support the theoretical analysis.