The paper studies the online bin packing problem in the context of untrusted advice. The authors propose a new algorithm called Robust-Reserve-Critical (Rrc) that can be tuned based on how much the algorithm trusts the advice.
The key highlights and insights are:
The authors introduce a new model of online computation with untrusted advice, where the advice can be adversarial. This is in contrast to previous advice models where the advice is always assumed to be correct.
For the bin packing problem, the authors show that existing algorithms with advice perform poorly when the advice is untrusted. To address this, they propose the Rrc algorithm that can be parameterized by a value α ∈ [0, 1] to control the trade-off between trusted and untrusted performance.
The Rrc algorithm is (r, f(r))-competitive, where r is the trusted competitive ratio and f(r) is the untrusted competitive ratio. The authors show that f(r) smoothly decreases as r increases, allowing the algorithm designer to hedge against untrusted advice by a small sacrifice in the trusted performance.
The authors also establish lower bounds on the trade-off between the advice size and the competitiveness of any online bin packing algorithm, demonstrating the limitations of using a bounded number of advice bits.
The results illustrate the importance of considering the trustworthiness of advice when designing online algorithms, and provide a framework for analyzing the robustness of online algorithms in the presence of potentially adversarial advice.
To Another Language
from source content
arxiv.org
Deeper Inquiries