Consensus Alternating Direction Method Of Multipliers Research Articles

Antenna Selection and Receive Beamforming for Multi-Functional Sparse Linear Array via Consensus ADMM

Antenna Selection and Receive Beamforming for Multi-Functional Sparse Linear Array via Consensus ADMM

FedAND: Federated Learning Exploiting Consensus ADMM by Nulling Drift

FedAND: Federated Learning Exploiting Consensus ADMM by Nulling Drift

Privacy-preserving distributed market mechanism for active distribution networks

Amidst the worldwide efforts to decarbonize power networks, Local Electricity Markets (LEMs) in distribution networks are gaining importance due to the increased adoption of renewable energy sources and prosumers. Considering that LEMs involve data exchange among independent entities, privacy and cybersecurity are some of the main practical challenges in LEM design. This paper proposes a secure market protocol using innovations from distributed optimization and Secure MultiParty Computation (SMPC). The considered LEM is formulated as an uncertainty-aware joint market for energy and reserves with affine balancing policies. To achieve scalability and enable the use of SMPC, market clearing is solved using the Consensus ADMM algorithm. Subsequently, the data exchange among participants via ADMM iterations is protected using the Shamir secret-sharing scheme to ensure privacy. The market protocol is further reinforced by a secure and verifiable settlement process that uses SMPC and ElGamal commitments to verify market quantities and by a secure recovery scheme for missing network measurements. Finally, the feasibility and performance of the proposed LEM are evaluated on a 15-bus test network.

A decentralized HC-ADMM approach for large antenna arrays

This work addresses the evolving landscape of internet of things (IoT) applications and large antenna array systems, where optimizing spectral efficiency and simplifying design complexities are crucial. Focusing on two key challenges, the study introduces a novel hybrid analog-digital transceiver strategy tailored for frequency-selective channels. By integrating Shannon and Hartley theorems, the approach enhances data transfer rates, thereby optimizing radio frequency (RF) chain utilization in large-scale antennas. To achieve a balance between transceiver performance and hardware complexity, the study employs a decentralized alternating direction method of multipliers (ADMM) framework. The proposed hybrid consensus ADMM algorithm (HC-ADMM) ensures efficient convergence in decentralized optimization scenarios. Comparative analyses with ADMM and existing system transceiver optimization (ESTO) models highlight HC-ADMM's superior performance across key metrics such as spectral efficiency, efficiency per cell, total efficiency, and optimal scheduling of user equipment (UEs). Particularly notable is HC-ADMM's advanced optimization capabilities as the number of transmit antennas increases, positioning it as a promising approach for enhancing overall communication network performance.

Holistic coordination of transactive energy and carbon emission right trading for heterogenous networked multi-energy microgrids: A fully distributed adaptive consensus ADMM approach

The paradigm of multi-energy microgrids (MEMGs) with internal energy sharing and trading is considered a promising option to empower low-carbon energy transitions. In this work, a holistic and efficient optimization framework is proposed to coordinate electricity, heat, and carbon emission right (CER) trading activities among multiple MEMGs. The proposed framework captures the heterogeneity of MEMGs, peer-to-peer transactive energy of different forms, and CER trading and network constraints together in a holistic fashion. To preserve the privacy of MEMGs and ensure transparency of CER trading, a fully distributed adaptive consensus alternating direction method of multipliers (AC-ADMM) is proposed, based on the combination of parallel computation capability of C-ADMM and the penalty parameters adaptive tuning mechanism. Case studies demonstrate the effectiveness of the proposed AC-ADMM method and reveal significant economic and environmental benefits for MEMGs by coordinating different forms of energy and CER trading in local transactive markets, while satisfying the network constraints. The superior convergence performance of the proposed method is also validated against conventional ADMM and C-ADMM methods.

Improved Consensus ADMM for Cooperative Motion Planning of Large-Scale Connected Autonomous Vehicles with Limited Communication

Improved Consensus ADMM for Cooperative Motion Planning of Large-Scale Connected Autonomous Vehicles with Limited Communication

Distributed model predictive control based on the alternating directions method of multipliers applied to voltage and frequency control in power systems

AbstractWe present a straightforward way to solve a model predictive control problem for a power network system given as a nonlinear differential‐algebraic equation (DAE) in a distributed way using the consensus alternating directions method of multipliers (consensus ADMM) algorithm. While no convergence‐ or stability results are available for fully nonlinear DAE models, this gives unprecedented experimental evidence that power network systems of the presented structure allow to be controlled in this way, unlocking the numerous combined advantages of distributed and predictive control schemes in the context of energy distribution networks, as well as broadening the field of use for the consensus ADMM algorithm to nonlinear DAE models.

Decentralized iLQR for Cooperative Trajectory Planning of Connected Autonomous Vehicles via Dual Consensus ADMM

Decentralized iLQR for Cooperative Trajectory Planning of Connected Autonomous Vehicles via Dual Consensus ADMM

Distributed Multirobot Task Assignment via Consensus ADMM

In this article, we present a distributed algorithm to solve a class of multirobot task assignment problems. We formulate task assignment as a mathematical optimization and solve for optimal solutions with a variant of the consensus alternating direction method of multipliers (C-ADMM). We provide C-ADMM-based algorithms for both the primal and dual problem formulations and show the advantages of each form depending on the problem specifics. In our algorithm, each robot solves a series of local optimization problems and communicates the results to its local neighbors, ultimately converging to an optimal task assignment in problems with linear objective functions and an optimal solution of the relaxed problem in convex problems. While many other distributed algorithms require a central station for their implementation, in our algorithm, each robot only communicates with its one-hop neighbors. In linear task assignment problems, our algorithm converges to the optimal task assignment, unlike many other distributed algorithms for this problem, which yield suboptimal solutions. We demonstrate our algorithms in task assignment problems over a variety of communication network topologies, where we show that our inexact dual algorithm is at least 60% faster than other distributed algorithms, which produce an optimal task assignment. In addition, our dual algorithm attains a 69% speedup in computation time compared to a notable distributed variant of the Hungarian method (Chopra et al., 2017). We also apply our algorithm to a multi-unmanned-aerial-vehicle persistent surveillance problem, showing its suitability for problems involving periodic task assignments.

Communication-Efficient Federated Learning with Adaptive Consensus ADMM

This paper proposes utilizing federated learning (FL), a distributed learning paradigm, to process large, decentralized, and heterogeneous edge data in the context of Internet of Things (IoT) devices. However, heterogeneity and high communication costs are two primary challenges that hinder the efficacy of federated learning. To overcome these challenges, we have designed an algorithm, FedACADMM, which applies the adaptive consensus alternating direction method of multipliers (ACADMM) to federated learning clients (i.e., the edge mobile devices) to tackle the heterogeneity problem in federated networks. Importantly, the cost per round of communication for FedACADMM remains consistent with FedAvg and FedProx without adding any extra workload. Furthermore, our experimental results demonstrate that FedACADMM outperforms baseline methods with a realistic set of federated datasets, displaying enhanced convergence robustness. Notably, in highly heterogeneous scenarios, FedACADMM exhibits significant stability and rapid convergence, requiring at least 75% fewer communication rounds to achieve the target accuracy.

Sparsity promoting decentralized learning strategies for radio tomographic imaging using consensus based ADMM approach

Radio tomographic imaging (RTI) has wide applications in the detection and tracking of objects that do not require any sensor to be attached to the object. Consequently, it leads to device-free localization (DFL). RTI uses received signal strength (RSS) at different sensor nodes for imaging purposes. The attenuation maps, known as spatial loss fields (SLFs), measure the power loss at each pixel in the wireless sensor network (WSN) of interest. These SLFs help us to detect obstacles and aid in the imaging of objects. The centralized RTI system requires the information of all sensor nodes available at the fusion centre (FC), which in turn increases the communication overhead. Furthermore, the failure of links may lead to improper imaging in the RTI system. Hence, a distributed approach for the RTI system resolves such problems. In this paper, a consensus-based distributed strategy is used for distributed estimation of the SLF. The major contribution of this work is to propose a fully decentralized RTI system by using a consensus-based alternating direction method of multipliers (ADMM) algorithm to alleviate the practical issues with centralized and distributed incremental strategies. We proposed distributed consensus ADMM (DCADMM-RTI) and distributed sparse consensus ADMM (DSCADMM-RTI) for the RTI system to properly localize targets in a distributed fashion. Furthermore, the effect of quantization noise is verified by using the distributed consensus algorithms while sharing the quantized data among the neighbourhoods.

Widely Distributed Radar Imaging: Unmediated ADMM Based Approach

This paper presents a novel approach to reconstruct a unique image of an observed scene via synthetic apertures (SA) generated by employing widely distributed radar sensors. The problem is posed as a constrained optimization problem in which the global image which represents the aggregate view of the sensors is a decision variable. While the problem is designed to promote a sparse solution for the global image, it is constrained such that a relationship with local images that can be reconstructed using the measurements at each sensor is respected. Two problem formulations are introduced by stipulating two different establishments of that relationship. The proposed formulations are designed according to consensus ADMM (CADMM) and sharing ADMM (SADMM), and their solutions are provided accordingly as iterative algorithms. We drive the explicit variable updates for each algorithm in addition to the recommended scheme for hybrid parallel implementation on the distributed sensors and a central processing unit. Our algorithms are validated and their performance is evaluated by exploiting the Civilian Vehicles Dome dataset to realize different scenarios of practical relevance. Experimental results show the effectiveness of the proposed algorithms, especially in cases with limited measurements.

Convergence analysis of consensus-ADMM for general QCQP

We analyze the convergence properties of the consensus-alternating direction method of multipliers (ADMM) for solving general quadratically constrained quadratic programs. We prove that the augmented Lagrangian function value is monotonically non-increasing as long as the augmented Lagrangian parameter is chosen to be sufficiently large. Simulation results show that the augmented Lagrangian function is bounded from below when the matrix in the quadratic term of the objective function is positive definite. In such a case, the consensus-ADMM is convergent.

Cooperative P2P Transaction Framework Between DSO and PMO Based on Consensus ADMM Against Path-Sharing Distribution Network Congestion

Cooperative P2P Transaction Framework Between DSO and PMO Based on Consensus ADMM Against Path-Sharing Distribution Network Congestion

Superresolution Composite SAR Imaging Method by Hierarchical Pattern With Attributed Scattering Priors

Compared with the ideal point scattering model, the attributed scattering center (ASC) model provides concise and physically relevant description of the man-made targets, such as the vehicles and ships, giving an efficient way to interpret the measurements of high-frequency and wide-angle synthetic aperture radar (SAR). Since the ASC model is complicated, it gives rise to a high-dimensional model parameter estimation problem, leading to a heavy computational burden. Aimed at this problem, in this article, a novel superresolution composite SAR imaging algorithm is proposed based on sparse representation, in which the ASC attributes are estimated by a hierarchical pattern for reducing the computational cost. In the proposed algorithm, the ASC model parameters, classified into three categories, are estimated coarsely via minimum variance pursuit (MVP). Then these coarse estimates are refined jointly using the consensus alternating direction method of multipliers (CADMM) optimization for polarimetric measurements. The spectrum extrapolation and replacement are performed for superresolution and reducing the impact of model mismatch, followed by a fast Fourier transform (FFT)-based operation to efficiently obtain the final superresolution image. The proposed approach not only possesses the ability of superresolution reconstruction with the target nonideal scattering features’ enhancement but also offers the physically relevant attributes on the premise of estimation accuracy improvement and calculation capacity reduction. Extensive experiments are conducted to corroborate the effectiveness of the proposed algorithm.

Optimal sizing of residential battery energy storage systems for long-term operational planning

Appropriate battery storage capacity plays an important role in the performance and cost of residential energy systems. However, the load demand and renewable energy generation vary seasonally. To address the long-term operational planning problem of battery energy storage, two battery sizing methods are developed based on the consensus alternating direction method of multipliers (C-ADMM). The residential system layout and convex battery model considering cycling aging are first established. Then, for the one-year horizon planning, C-ADMM decomposes the original large-scale problem into 365 sub-problems. The global optimal battery size and operation strategy are obtained by coordinating these sub-problems in parallel until convergence. In addition, to obtain the optimal battery size based on receding horizon control (RHC) strategies in a year, a new bi-level optimization method is developed, which uses the particle swarm optimization (PSO) algorithm in the outer loop and C-ADMM in the inner loop. Thus the inner loop can be accelerated through parallel computing. The results show that the long-term storage planning problem of residential systems can be solved quickly through C-ADMM due to the parallel computing capability. Furthermore, the optimal battery size calculated under the RHC strategy in a year can contribute to more economic benefits than the global battery size when applied online.

Comparison of Decentralized ADMM Optimization Algorithms for Power Allocation in Modular Fuel Cell Vehicles

The advanced modular powertrains are envisioned as primary part of future hybrid fuel cell vehicles (FCVs). The existing papers in the literature solely cope with the hardware side of modularity, while the software side is also vital to capitalize on the total capacity of these powertrains. Driven by this motivation, this article puts forward a comparative study of two novel decentralized convex optimization frameworks based on alternating direction method of multipliers (ADMM) to solve a multi-objective power allocation strategy (PAS) problem in a modular FCV (MFCV). The MFCV in this article is composed of two fuel cell (FC) stacks and a battery pack. Despite the existing centralized strategies for such a modular system, this manuscript proposes two decentralized PASs (Dec-PASs) based on Consensus ADMM (C-ADMM) and Proximal Jacobian ADMM (PJ-ADMM) to bridge the gap regarding the appreciation of modularity in software terms. Herein, after formulating the central PAS optimization problem, the principle of utilizing such decentralized algorithms is presented in detail. Subsequently, the performance of the proposed Dec-PASs is examined through several numerical simulations as well as experiments on a developed small-scale test bench. The obtained results illustrate that decomposition into decentralized forms enables solving the complex PAS optimization problem faster and provides modularity and flexibility. Furthermore, the proposed Dec-PASs can cope with fault and malfunction and thus augment the durability and robustness of modular powertrain systems.

Dynamic ISAR Imaging Method for Multiple Moving Vehicles Based on OMP-CADMM

Inverse synthetic aperture radar (ISAR) technology has attracted considerable attention in smart traffic monitoring, owing to the superiority of high-resolution imaging and tracking for moving targets in all-weather. However, in practical application cases, it is hard to obtain a globally-focused image of multiple vehicles by traditional ISAR imaging techniques due to the nonuniform motions of vehicles. Hence, a dynamic ISAR imaging method for multiple moving vehicles based on the joint of orthogonal matching pursuit (OMP) and consensus alternating direction method of multipliers (CADMM) is proposed in this paper. First, the preprocessing for raw data is performed in the wavenumber domain to simplify the imaging task into linear phase error estimation and distributed image recovery. Based on the sparsity of moving vehicles, the OMP method is utilized to estimate the dynamic motion phase error of consecutive frames data for different moving vehicles with less computational overhead. Then, the dynamic ISAR imaging problems of multiple moving vehicles can be transformed into the distributed imaging problem with variously estimated phases. Motivated by this fact, the CADMM optimization framework is exploited for the distributed ISAR imaging problem to obtain a globally-focused result according to the consensus constraint characteristic of multiple vehicles. Lastly, both the simulation and real data experiments are conducted to verify the performance and feasibility of the OMP-CADMM method. Furthermore, the experimental results show that the proposed method is capable of dynamic imaging and de-noising for multiple vehicles without signal separation.

재생에너지가 포함된 연계계통에서 경제급전을 위한 컨센서스 ADMM 응용

This paper applies a consensus alternating direction method of multipliers (ADMM) for the economic dispatch problem in a multi-region system with renewable energy. The proposed algorithm decides the optimal generation of each area including consensus buses, and calculates the average generation of consensus buses at each iteration. The solutions from consensus ADMM are compared with the Newton-Raphson method, demonstrating superior performance because it only uses the local information. Furthermore, the privacy of each system operator can be guaranteed.

Distributed Collaborative Optimization of a Multi-Region Integrated Energy System Based on Edge Computing Unit

The coordinated optimization scheduling of the integrated energy systems is vital in multi-energy complementarity and hierarchical utilization. However, the centralized optimization is inferior to the distributed optimization of the large-scale multiregion integrated energy system (MRIES) in data processing capacity and information security. This study proposes a distributed computing architecture based on the edge computing unit (ECU), which takes the energy hub as the main body and sets the partitioning principle and method of MRIES. The ECU can finally realize the whole-system collaborative optimization of MRIES, which contains electrical, natural gas, and district heating networks through internal autonomous optimization and boundary information interaction with the cloud computing center. At the same time, an improved nested algorithm based on the consensus-alternating direction method of multipliers is proposed, which ensures the convergence of the mixed-integer linear program and effectively improves the convergence speed. Combining the advantages of the model and algorithm provides a theoretical and algorithmic support for the optimization research of the MRIES.