Wait-Less Offline Tuning and Re-solving for Online Decision Making
Jingruo Sun, Wenzhi Gao, Ellen Vitercik, Yinyu Ye

TL;DR
This paper introduces a 'wait-less' online decision-making algorithm that combines LP-based and first-order methods, achieving low regret with improved computational efficiency for large-scale online linear programming problems.
Contribution
The paper proposes a novel algorithm that periodically re-solves LP subproblems and runs a first-order method in parallel, balancing regret guarantees and computational efficiency.
Findings
Achieves $oxed{ ext{O}( ext{log}(T/f) + extsqrt{f})}$ regret bound.
Balances computational efficiency with regret guarantees.
Provides a practical approach for large-scale online decision problems.
Abstract
Online linear programming (OLP) has found broad applications in revenue management and resource allocation. State-of-the-art OLP algorithms achieve low regret by repeatedly solving linear programming (LP) subproblems that incorporate updated resource information. However, LP-based methods are computationally expensive and often inefficient for large-scale applications. In contrast, recent first-order OLP algorithms are more computationally efficient but typically suffer from worse regret guarantees. To address these shortcomings, we propose a new algorithm that combines the strengths of LP-based and first-order OLP methods. The algorithm re-solves the LP subproblems periodically at a predefined frequency and uses the latest dual prices to guide online decision-making. In addition, a first-order method runs in parallel during each interval between LP re-solves, smoothing resource…
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Taxonomy
TopicsBusiness Process Modeling and Analysis · Complex Systems and Decision Making
