Exponentially Weighted Algorithm for Online Network Resource Allocation with Long-Term Constraints

• URL: http://arxiv.org/abs/2405.02373v1
• Date: Fri, 3 May 2024 10:12:40 GMT
• Title: Exponentially Weighted Algorithm for Online Network Resource Allocation with Long-Term Constraints
• Authors: Ahmed Sid-Ali, Ioannis Lambadaris, Yiqiang Q. Zhao, Gennady Shaikhet, Amirhossein Asgharnia,
• Abstract summary: This paper studies an online optimal resource reservation problem in communication networks with job transfers. We propose a novel algorithm based on a randomized exponentially weighted method that encompasses long-term constraints.
• Score: 0.6466206145151128
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper studies an online optimal resource reservation problem in communication networks with job transfers where the goal is to minimize the reservation cost while maintaining the blocking cost under a certain budget limit. To tackle this problem, we propose a novel algorithm based on a randomized exponentially weighted method that encompasses long-term constraints. We then analyze the performance of our algorithm by establishing an upper bound for the associated regret and the cumulative constraint violations. Finally, we present numerical experiments where we compare the performance of our algorithm with those of reinforcement learning where we show that our algorithm surpasses it.

Related papers

• Lower Bounds and Optimal Algorithms for Non-Smooth Convex Decentralized Optimization over Time-Varying Networks [57.24087627267086]
  We consider the task of minimizing the sum of convex functions stored in a decentralized manner across the nodes of a communication network. Lower bounds on the number of decentralized communications and (sub)gradient computations required to solve the problem have been established. We develop the first optimal algorithm that matches these lower bounds and offers substantially improved theoretical performance compared to the existing state of the art.
  arXiv  Detail & Related papers  (2024-05-28T10:28:45Z)
• Efficient Constraint Generation for Stochastic Shortest Path Problems [0.0]
  We present an efficient version of constraint generation for Shortest Path Problems (SSPs) This technique allows algorithms to ignore sub-optimal actions and avoid computing their costs-to-go. Our experiments show that CG-iLAO* ignores up to 57% of iLAO*'s actions and it solves problems up to 8x and 3x faster than LRTDP and iLAO*.
  arXiv  Detail & Related papers  (2024-01-26T04:00:07Z)
• High-dimensional Contextual Bandit Problem without Sparsity [8.782204980889077]
  We propose an explore-then-commit (EtC) algorithm to address this problem and examine its performance. We derive the optimal rate of the ETC algorithm in terms of $T$ and show that this rate can be achieved by balancing exploration and exploitation. We introduce an adaptive explore-then-commit (AEtC) algorithm that adaptively finds the optimal balance.
  arXiv  Detail & Related papers  (2023-06-19T15:29:32Z)
• Safe Online Bid Optimization with Return-On-Investment and Budget Constraints subject to Uncertainty [87.81197574939355]
  We study the nature of both the optimization and learning problems. We provide an algorithm, namely GCB, guaranteeing sublinear regret at the cost of a potentially linear number of constraints violations. More interestingly, we provide an algorithm, namely GCB_safe(psi,phi), guaranteeing both sublinear pseudo-regret and safety w.h.p. at the cost of accepting tolerances psi and phi.
  arXiv  Detail & Related papers  (2022-01-18T17:24:20Z)
• Online Allocation with Two-sided Resource Constraints [44.5635910908944]
  We consider an online allocation problem subject to lower and upper resource constraints, where the requests arrive sequentially. We propose a new algorithm that obtains $1-O(fracepsilonalpha-epsilon)$ -competitive ratio for the offline problems that know the entire requests ahead of time.
  arXiv  Detail & Related papers  (2021-12-28T02:21:06Z)
• Simultaneously Achieving Sublinear Regret and Constraint Violations for Online Convex Optimization with Time-varying Constraints [26.473560927031176]
  We develop a novel virtual-queue-based online algorithm for online convex optimization (OCO) problems with long-term and time-varying constraints. Our algorithm is the first parameter-free algorithm to simultaneously achieve sublinear dynamic regret and constraint violations.
  arXiv  Detail & Related papers  (2021-11-15T12:23:31Z)
• Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks [79.16773494166644]
  We consider the task of minimizing the sum of smooth and strongly convex functions stored in a decentralized manner across the nodes of a communication network. We design two optimal algorithms that attain these lower bounds. We corroborate the theoretical efficiency of these algorithms by performing an experimental comparison with existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-06-08T15:54:44Z)
• An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits [129.1029690825929]
  We introduce a novel algorithm improving over the state-of-the-art along multiple dimensions. We establish minimax optimality for any learning horizon in the special case of non-contextual linear bandits.
  arXiv  Detail & Related papers  (2020-10-23T09:12:47Z)
• Coordinated Online Learning for Multi-Agent Systems with Coupled Constraints and Perturbed Utility Observations [91.02019381927236]
  We introduce a novel method to steer the agents toward a stable population state, fulfilling the given resource constraints. The proposed method is a decentralized resource pricing method based on the resource loads resulting from the augmentation of the game's Lagrangian.
  arXiv  Detail & Related papers  (2020-10-21T10:11:17Z)
• Logarithmic Regret for Adversarial Online Control [56.12283443161479]
  We give the first algorithm with logarithmic regret for arbitrary adversarial disturbance sequences. Our algorithm and analysis use a characterization for the offline control law to reduce the online control problem to (delayed) online learning.
  arXiv  Detail & Related papers  (2020-02-29T06:29:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.