Long wait times, coordination, and integration are persistent issues in many health systems, hindering effective management of client waitlists and delaying treatment. The traditional approach of independent schedulin...
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Long wait times, coordination, and integration are persistent issues in many health systems, hindering effective management of client waitlists and delaying treatment. The traditional approach of independent scheduling at each center does not allow for a coordinated response to demand fluctuations. An Integrated Online Booking system (IOB) for outpatient services is designed in this study, presenting the first central intake-based appointment system integrated with an optimization approach and a novel decentralized and distributed appointment system. This system considers the independence of centers while better responding to demand and improving overall system performance. The optimization component of the IOB system is expanded in this paper to include multiple sites, patient preferences, priorities, and wait time targets. It becomes a large-scale problem as all requests from a geographical region are combined into one stream. A decomposable algorithm based on the Alternating Direction Method of Multipliers (ADMM) is used for this system. The study employs real large-scale MRI data from all hospitals in Ontario, Canada, to demonstrate the effectiveness of the IOB system, providing insights into its potential impact on healthcare operations. The results indicate that the IOB system can outperform common appointment scheduling approaches in terms of reducing patient wait times, balancing utilization rates, improving referral patterns, and enhancing system efficiency. The proposed IOB system can bring added value to the market by speeding up diagnosis and treatment processes while improving the efficiency of healthcare systems. This platform can be considered as Software as a Service (SaaS), providing a scalable and accessible solution for managing appointments across various sectors, and leveraging blockchain technology to ensure secure, transparent, and tamper-proof appointment records. This study provides insights for decision-makers, highlighting the importance o
In distributed database systems, commit protocols are used to ensure the transaction atomicity. In the presence of failures, nonblocking commit protocols can guarantee the transaction atomicity without blocking the tr...
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In distributed database systems, commit protocols are used to ensure the transaction atomicity. In the presence of failures, nonblocking commit protocols can guarantee the transaction atomicity without blocking the transaction execution. A (resilient) decentralized nonblocking commit protocol (RDCP) is proper-ed for distributed database systems. This protocol is based on the hypercube network topology and is 'liub(log(2)(N)) - 2' resilient to node failures (N = number of system-nodes). The number of messages sent among the N nodes is 0(***(2)(2)(N)) which is only a factor of log(2)(N) over the message complexity lower bound 0(***(2)(N)) of decentralized commit protocols. Furthermore, RDCP is an optimistic nonblocking protocol. It aborts the transaction only when some nodes want to abort or some nodes fail before they make local decisions.
Due to the rapid growth of smart agents such as weakly connected computational nodes and sensors, developing decentralized algorithms that can perform computations on local agents becomes a major research direction. T...
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Due to the rapid growth of smart agents such as weakly connected computational nodes and sensors, developing decentralized algorithms that can perform computations on local agents becomes a major research direction. This paper considers the problem of decentralized principal components analysis (PCA), which is a statistical method widely used for data analysis. We introduce a technique called subspace tracking to reduce the communication cost, and apply it to power iterations. This leads to a decentralized PCA algorithm called DeEPCA, which has a convergence rate similar to that of the centralized PCA, while achieving the best communication complexity among existing decentralized PCA algorithms. DeEPCA is the first decentralized PCA algorithm with the number of communication rounds for each power iteration independent of target precision. Compared to existing algorithms, the proposed method is easier to tune in practice, with an improved overall communication cost. Our experiments validate the advantages of DeEPCA empirically.
This paper considers the decentralized convex optimization problem, which has a wide range of applications in large-scale machine learning, sensor networks, and control theory. We propose novel algorithms that achieve...
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This paper considers the decentralized convex optimization problem, which has a wide range of applications in large-scale machine learning, sensor networks, and control theory. We propose novel algorithms that achieve optimal computation complexity and near optimal communication complexity. Our theoretical results give affirmative answers to the open problem on whether there exists an algorithm that can achieve a communication complexity (nearly) matching the lower bound depending on the global condition number instead of the local one. Furthermore, the linear convergence of our algorithms only depends on the strong convexity of global objective and it does not require the local functions to be convex. The design of our methods relies on a novel integration of well-known techniques including Nesterov's acceleration, multi-consensus and gradient-tracking. Empirical studies show the outperformance of our methods for machine learning applications.
Under appropriate cooperation protocols and parameter choices, fully decentralized solutions for stochastic optimization have been shown to match the performance of centralized solutions and result in linear speedup (...
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ISBN:
(数字)9781509066315
ISBN:
(纸本)9781509066322
Under appropriate cooperation protocols and parameter choices, fully decentralized solutions for stochastic optimization have been shown to match the performance of centralized solutions and result in linear speedup (in the number of agents) relative to non-cooperative approaches in the strongly-convex setting. More recently, these results have been extended to the pursuit of first-order stationary points in non-convex environments. In this work, we examine in detail the dependence of second-order convergence guarantees on the spectral properties of the combination policy for non-convex multi agent optimization. We establish linear speedup in saddle-point escape time in the number of agents for symmetric combination policies and study the potential for further improvement by employing asymmetric combination weights. The results imply that a linear speedup can be expected in the pursuit of second-order stationary points, which exclude local maxima as well as strict saddle-points and correspond to local or even global minima in many important learning settings.
In this paper, we develop the robust transceiver optimization for the multiple-input single-output (MISO) interference channels where each transmitter (Tx) is equipped with multiple antennas and each single-antenna re...
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ISBN:
(纸本)9781509016990
In this paper, we develop the robust transceiver optimization for the multiple-input single-output (MISO) interference channels where each transmitter (Tx) is equipped with multiple antennas and each single-antenna receiver performs simultaneous wireless information and power transfer (SWIPT) based on a power-splitting architecture. Assuming imperfect channel state information (CSI) at the Txs, we design jointly optimal transmit beamforming and receive power-splitting scheme that minimizes the total transmission power under the worst-case signal-to-interference-plus-noise ratio (SINR) and energy harvesting (EH) constraints. When the channel uncertainties are bounded by ellipsoidal regions, we show that the worst-case SINR and EH constraints can be recast into quadratic matrix inequality forms, and the intended problem can be relaxed as a tractable semi-definite program. Furthermore, relying on the alternating direction method of multipliers (ADMM), we propose a decentralized algorithm capable of computing the optimal beamforming and power-splitting schemes with local CSI and limited information exchange among the Txs.
The popularity of mobile cloud computing has provisioned a new paradigm for mobile devices to offload their computation to remote mobile cloud data center for task execution. However, the mobile cloud data center may ...
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ISBN:
(纸本)9781509018949
The popularity of mobile cloud computing has provisioned a new paradigm for mobile devices to offload their computation to remote mobile cloud data center for task execution. However, the mobile cloud data center may suffer from dramatic energy consumption. To solve this problem, we propose to migrate the workload from remote cloud data center to nearby cloudlets, so as to relieve the pressure of cloud data center and save the energy consumption. Specifically, we propose a light-weight and decentralized algorithm based on the Alternating Direction Method of Multipliers (ADMM) algorithm to construct the migration scheme. Simulations show that our algorithm can fast converge in tens of iterations, and decrease the overall energy consumption.
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