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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 > No. 4: AAAI-22 Technical Tracks 4

A Lyapunov-Based Methodology for Constrained Optimization with Bandit Feedback

February 1, 2023

Authors

Semih Cayci

University of Illinois at Urbana-Champaign


Yilin Zheng

The Ohio State University


Atilla Eryilmaz

The Ohio State University


Proceedings:

No. 4: AAAI-22 Technical Tracks 4

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Technical Track on Constraint Satisfaction and Optimization

Downloads:

Download PDF

Abstract:

In a wide variety of applications including online advertising, contractual hiring, and wireless scheduling, the controller is constrained by a stringent budget constraint on the available resources, which are consumed in a random amount by each action, and a stochastic feasibility constraint that may impose important operational limitations on decision-making. In this work, we consider a general model to address such problems, where each action returns a random reward, cost, and penalty from an unknown joint distribution, and the decision-maker aims to maximize the total reward under a budget constraint B on the total cost and a stochastic constraint on the time-average penalty. We propose a novel low-complexity algorithm based on Lyapunov optimization methodology, named LyOn, and prove that for K arms it achieves square root of KBlog(B) regret and zero constraint-violation when B is sufficiently large. The low computational cost and sharp performance bounds of LyOn suggest that Lyapunov-based algorithm design methodology can be effective in solving constrained bandit optimization problems.

DOI:

10.1609/aaai.v36i4.20285


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36



Topics: AAAI

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