# An adaptive stochastic optimization algorithm for resource allocation

arXiv: https://arxiv.org/abs/1902.04376 · HTML: https://mlsanity.com/paper/1902.04376/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2019.

## Built on by (most cited first, 11 of 11)

- [Variance-reduced first-order methods for deterministically constrained stochastic nonconvex optimization with strong convergence guarantees](https://mlsanity.com/paper/2409.09906/) (2024)
- [Combinatorial Multi-armed Bandits for Resource Allocation](https://mlsanity.com/paper/2105.04373/) (2021)
- [Censored Semi-Bandits for Resource Allocation](https://mlsanity.com/paper/2104.05781/) (2021)
- [Online Sign Identification: Minimization of the Number of Errors in Thresholding Bandits](https://mlsanity.com/paper/2110.09133/) (2021)
- [First-order methods for stochastic and finite-sum convex optimization with deterministic constraints](https://mlsanity.com/paper/2506.20630/) (2025)
- [Making the most of your day: online learning for optimal allocation of time](https://mlsanity.com/paper/2102.08087/) (2021)
- [Stochastic Direct Search Method for Blind Resource Allocation](https://mlsanity.com/paper/2210.05222/) (2022)
- [Sequential Decision Problems with Weak Feedback](https://mlsanity.com/paper/2212.11603/) (2022)
- [Constant or logarithmic regret in asynchronous multiplayer bandits](https://mlsanity.com/paper/2305.19691/) (2023)
- [Allocating Divisible Resources on Arms with Unknown and Random Rewards](https://mlsanity.com/paper/2306.16578/) (2023)
- [Learning to Allocate Resources with Censored Feedback](https://mlsanity.com/paper/2602.06565/) (2026)

## Abstract (arXiv)

We consider the classical problem of sequential resource allocation where a decision maker must repeatedly divide a budget between several resources, each with diminishing returns. This can be recast as a specific stochastic optimization problem where the objective is to maximize the cumulative reward, or equivalently to minimize the regret. We construct an algorithm that is em adaptive to the complexity of the problem, expressed in term of the regularity of the returns of the resources, measured by the exponent in the Lojasiewicz inequality (or by their universal concavity parameter). Our parameter-independent algorithm recovers the optimal rates for strongly-concave functions and the classical fast rates of multi-armed bandit (for linear reward functions). Moreover, the algorithm improves existing results on stochastic optimization in this regret minimization setting for intermediate cases.