# Robust Optimization Approach and Learning Based Hide-and-Seek Game for Resilient Network Design

arXiv: https://arxiv.org/abs/2602.11854 · HTML: https://mlsanity.com/paper/2602.11854/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 36 references that are in the map.
- First posted on arXiv 12 Feb 2026.

## Builds on (most cited first, 5 of 5)

- [Satellite Communications in the New Space Era: A Survey and Future Challenges](https://mlsanity.com/paper/2002.08811/) (2020)
- [Benchmarking Problems for Robust Discrete Optimization](https://mlsanity.com/paper/2201.04985/) (2022)
- [Robust Combinatorial Optimization Problems Under Budgeted Interdiction Uncertainty](https://mlsanity.com/paper/2307.08525/) (2023)
- [Recoverable robust shortest path problem under interval budgeted uncertainty representations](https://mlsanity.com/paper/2401.05715/) (2024)
- [A Robust Optimization Approach for Regenerator Placement in Fault-Tolerant Networks Under Discrete Cost Uncertainty](https://mlsanity.com/paper/2602.11058/) (2026)

## Abstract (arXiv)

We study the design of resilient and reliable communication networks in which a signal can be transferred only up to a limited distance before its quality falls below an acceptable threshold. When excessive signal degradation occurs, regeneration is required through regenerators installed at selected network nodes. In this work, both network links and nodes are subject to uncertainty. The installation costs of regenerators are modeled using a budgeted uncertainty set. In addition, link lengths follow a dynamic budgeted uncertainty set introduced in this paper, where deviations may vary over time. Robust optimization seeks solutions whose performance is guaranteed under all scenarios represented by the underlying uncertainty set. Accordingly, the objective is to identify a minimum-cost subset of nodes for regenerator deployment that ensures full network connectivity, even under the worst possible realizations of uncertainty. To solve the problem, we first formulate it within a robust optimization framework, and then develop scalable solution methods based on column-and-constraint generation, Benders decomposition, and iterative robust optimization. In addition, we formulate a learning-based hide-and-seek game to further analyze the problem structure. The proposed approaches are evaluated against classical static budgeted robust models and deterministic worst-case formulations. Both theoretical analysis and computational results demonstrate the effectiveness and advantages of our methodology.