Discrete-Time Algorithm for Distributed Unconstrained Optimization Problem With Finite-Time Computations

Liu, Hongzhe; Yu, Wenwu

doi:10.1109/tcsii.2020.2987947

Cited by 10 publications

(3 citation statements)

References 31 publications

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“…where || * || F denotes the Frobenius norm of matrix. The problem in ( 29) is a classical unconstrained optimization problem, and the closed-form solution of this problem group is the maximum eigenvector of Ŵr,m and Âc,m multiplied by the square root of the maximum eigenvalue [46]. Suppose that the eigenvalue decomposition of Ŵr,m and Âc,m is expressed as…”

Section: Constraint Relaxation-based Robust Ofdm Waveform (Row-cr) De...mentioning

confidence: 99%

Robust Multiplexing Waveform Design for Integrated OFDM Radar and Communication via Complex Weight Optimization

Wang,

Cao,

Yeo

et al. 2023

Remote Sensing

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Compared to traditional single orthogonal frequency division multiplexing (OFDM) radar and OFDM communication systems, integrated OFDM radar and communication systems have the advantages of improved sharing of scarce spectrum resources, a simple hardware structure and a reduced interference between signals. In this paper, a constraint relaxation-based robust OFDM multiplexing waveform (ROW-CR) design method for integrated radar and communication systems is proposed. Considering the influence of correlated clutter and jamming signals, transmission and reception models of different system platforms are established by allocating subcarrier complex weights of different antenna signals so as to determine radar conditional mutual information and communication channel capacity parameters. Meanwhile, a restricted closed model is introduced through the limit range of the target frequency response. Then, a robust OFDM waveform optimization problem for integrated radar and communication systems is constructed by the minimax criterion, and the closed-form solution is obtained by adopting an improved method based on trace function properties and constraint relaxation, resulting in a better radar and communication performance trade-off. In addition, a parameter hierarchical optimization-based robust OFDM waveform (ROW-PHO) design method is further explored to reduce the computational complexity, and this method can also ensure a low system performance loss. Finally, the numerical simulation results verify the effectiveness of the proposed methods.

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Section: Constraint Relaxation-based Robust Ofdm Waveform (Row-cr) De...mentioning

confidence: 99%

Robust Multiplexing Waveform Design for Integrated OFDM Radar and Communication via Complex Weight Optimization

Wang,

Cao,

Yeo

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Numerous studies have yielded positive outcomes on distributed optimization problems [13][14][15][16][17][18]. Depending on whether a system is discrete or continuous, the majority of existing distributed algorithms fall into one of two groups: the discrete-time distributed algorithm and the continuous-time one.…”

Section: Introductionmentioning

confidence: 99%

Fixed-Time Distributed Optimization for Multi-Agent Systems with Input Delays and External Disturbances

Jiang

2022

Mathematics

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This study concentrates on a fixed-time distributed optimization problem for multi-agent systems (MASs) with input delay and external disturbances. First, by adopting the Artstein model reduction technique, the time-delay system is first transformed into a delay-free one, and external disturbances are then effectively eliminated by using an integral sliding mode control strategy. Second, a new centralized optimization mechanism is developed that allows all agents to reach the same state in a fixed time and then converge to the optimal value of the global objective function. Meanwhile, the optimization problem is extended to switching topologies. Moreover, as the gradient information of the global objective function is difficult to obtain in advance, we construct a decentralized optimization protocol that enables all agents to acquire the same state in a certain amount of time while also optimizing the global optimization problem. Finally, two numerical simulations are presented to validate the effectiveness and reliability of the developed control strategy.

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“…More specifically, real-valued messages assume channels of infinite capacity and asymptotic convergence means that the optimal is obtained as time approaches infinity. If they operate over a limited time-frame, then they terminate in the proximity of the optimal solution [6], [14] This is a direct consequence of the asymptotic nature of their operation, which does not provide finite-time convergence guarantees.…”

Section: Introductionmentioning

confidence: 99%

Optimal CPU Scheduling in Data Centers via a Finite-Time Distributed Quantized Coordination Mechanism

Rikos¹,

Grammenos²,

Kalyvianaki³

et al. 2021

2021 60th IEEE Conference on Decision and Control (CDC)

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We propose two distributed iterative algorithms that can be used to solve, in finite time, the distributed optimization problem over quadratic local cost functions in large-scale networks. The first algorithm exhibits synchronous operation whereas the second one exhibits asynchronous operation. Both algorithms share salient features. Specifically, the algorithms operate exclusively with quantized values, which means that the information stored, processed and exchanged between neighboring nodes is subject to deterministic uniform quantization. The algorithms rely on event-driven updates in order to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage. Finally, once the algorithms converge, nodes distributively terminate their operation. We prove that our algorithms converge in a finite number of iterations to the exact optimal solution depending on the quantization level, and we present applications of our algorithms to (i) optimal task scheduling for data centers, and (ii) global model aggregation for distributed federated learning. We provide simulations of these applications to illustrate the operation, performance, and advantages of the proposed algorithms. Additionally, it is shown that our proposed algorithms compare favorably to algorithms in the current literature. Quantized communication and asynchronous updates increase the required time to completion, but finite-time operation is maintained.

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Discrete-Time Algorithm for Distributed Unconstrained Optimization Problem With Finite-Time Computations

Cited by 10 publications

References 31 publications

Robust Multiplexing Waveform Design for Integrated OFDM Radar and Communication via Complex Weight Optimization

Robust Multiplexing Waveform Design for Integrated OFDM Radar and Communication via Complex Weight Optimization

Fixed-Time Distributed Optimization for Multi-Agent Systems with Input Delays and External Disturbances

Optimal CPU Scheduling in Data Centers via a Finite-Time Distributed Quantized Coordination Mechanism

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