Stochastic Shortest Path with Sparse Adversarial Costs

A recent study published on arXiv addresses the adversarial Stochastic Shortest Path problem with a focus on sparse costs (F1). The research critiques existing performance bounds derived from Online Mirror Descent methods (F2), noting that while these bounds are optimal in worst-case scenarios (F3), they fall short in leveraging the benefits of sparsity when only a limited number of costs are involved (F4). This suggests that current theoretical guarantees may not fully capture the potential improvements achievable in settings where cost occurrences are infrequent. The study highlights a gap in the literature regarding how sparsity can be exploited to enhance algorithmic performance beyond established worst-case analyses. These findings contribute to ongoing discussions in machine learning and optimization communities about refining models to better reflect practical problem structures. The work complements related research efforts documented on arXiv, emphasizing the importance of adapting theoretical tools to more nuanced cost environments.