Generalized Parametric Path Problems

37th Conference on Uncertainty in Artificial Intelligence (UAI) 2021
February, 2021


@misc{chatterjee2021generalized, title={Generalized Parametric Path Problems}, author={Prerona Chatterjee and Kshitij Gajjar and Jaikumar Radhakrishnan and Girish Varma}, year={2021}, eprint={2102.12886}, archivePrefix={arXiv}, primaryClass={cs.DS} }


Parametric path problems arise independently in diverse domains, ranging from transportation to finance, where they are studied under various assumptions. We formulate a general path problem with relaxed assumptions, and describe how this formulation is applicable in these domains. We study the complexity of the general problem, and a variant of it where preprocessing is allowed. We show that when the parametric weights are linear functions, algorithms remain tractable even under our relaxed assumptions. Furthermore, we show that if the weights are allowed to be non-linear, the problem becomes NP-hard. We also study the mutli-dimensional version of the problem where the weight functions are parameterized by multiple parameters. We show that even with two parameters, the problem is NP-hard.