Capacity Provisioning Motivated Online Non-Convex Optimization Problem with Memory and Switching Cost

2024-03-26 · Rahul Vaze, Jayakrishnan Nair

An online non-convex optimization problem is considered where the goal is to minimize the flow time (total delay) of a set of jobs by modulating the number of active servers, but with a switching cost associated with changing the number of active servers over time. Each job can be processed by at most one fixed speed server at any time. Compared to the usual online convex optimization (OCO) problem with switching cost, the objective function considered is non-convex and more importantly, at each time, it depends on all past decisions and not just the present one. Both worst-case and stochastic inputs are considered; for both cases, competitive algorithms are derived.

📄 PDF Abstract BibTeX arXiv:2403.17480

Code (0)

등록된 구현이 없습니다.

Methods 이 논문이 사용한 방법론

SET Dynamic Sparse Training method where weight mask is updated randomly periodically

SPEED The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene…

Bi-Level Online Provisioning and Scheduling with Switching Costs and Cross-Level Constraints

2026-01-26 · Jialei Liu, C. Emre Koksal, Ming Shi arxiv

We study a bi-level online provisioning and scheduling problem motivated by network resource allocation, where provisioning decisions are made at a slow time scale while queue-/state-dependent scheduling is performed at …

Optimal Hidden-Target Learning for Online Inventory Optimization on General Convex Sets

2026-06-12 · Anthony Pineci, Yunzong Xu arxiv

Online inventory optimization (OIO) is online convex optimization with physical memory: inventory carryover makes the feasible action set depend on the past. A natural principle, used in stochastic inventory learning and…

Multi-Objective Provisioning of Network Slices using Deep Reinforcement Learning

2022-07-27 · Chien-Cheng Wu, Vasilis Friderikos, Cedomir Stefanovic

Network Slicing (NS) is crucial for efficiently enabling divergent network applications in next generation networks. Nonetheless, the complex Quality of Service (QoS) requirements and diverse heterogeneity in network ser…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

A Convergence Analysis of Approximate Message Passing with Non-Separable Functions and Applications to Multi-Class Classification

2024-02-13 · Burak Çakmak, Yue M. Lu, Manfred Opper

Motivated by the recent application of approximate message passing (AMP) to the analysis of convex optimizations in multi-class classifications [Loureiro, et. al., 2021], we present a convergence analysis of AMP dynamics…

Multi-class Classification

Predictive Online Convex Optimization

2019-05-15 · Antoine Lesage-Landry, Iman Shames, Joshua A. Taylor

We incorporate future information in the form of the estimated value of future gradients in online convex optimization. This is motivated by demand response in power systems, where forecasts about the current round, e.g.…