2 research outputs found
Improved Learning-Augmented Algorithms for the Multi-Option Ski Rental Problem via Best-Possible Competitive Analysis
In this paper, we present improved learning-augmented algorithms for the
multi-option ski rental problem. Learning-augmented algorithms take ML
predictions as an added part of the input and incorporates these predictions in
solving the given problem. Due to their unique strength that combines the power
of ML predictions with rigorous performance guarantees, they have been
extensively studied in the context of online optimization problems. Even though
ski rental problems are one of the canonical problems in the field of online
optimization, only deterministic algorithms were previously known for
multi-option ski rental, with or without learning augmentation. We present the
first randomized learning-augmented algorithm for this problem, surpassing
previous performance guarantees given by deterministic algorithms. Our
learning-augmented algorithm is based on a new, provably best-possible
randomized competitive algorithm for the problem. Our results are further
complemented by lower bounds for deterministic and randomized algorithms, and
computational experiments evaluating our algorithms' performance improvements.Comment: 23 pages, 1 figur