Electrical capacitance tomography(ECT)has been applied to two-phase flow measurement in recent years.Image reconstruction algorithms play an important role in the successful applications of ECT.To solve the ill-posed ...Electrical capacitance tomography(ECT)has been applied to two-phase flow measurement in recent years.Image reconstruction algorithms play an important role in the successful applications of ECT.To solve the ill-posed and nonlinear inverse problem of ECT image reconstruction,a new ECT image reconstruction method based on fast linearized alternating direction method of multipliers(FLADMM)is proposed in this paper.On the basis of theoretical analysis of compressed sensing(CS),the data acquisition of ECT is regarded as a linear measurement process of permittivity distribution signal of pipe section.A new measurement matrix is designed and L1 regularization method is used to convert ECT inverse problem to a convex relaxation problem which contains prior knowledge.A new fast alternating direction method of multipliers which contained linearized idea is employed to minimize the objective function.Simulation data and experimental results indicate that compared with other methods,the quality and speed of reconstructed images are markedly improved.Also,the dynamic experimental results indicate that the proposed algorithm can ful fill the real-time requirement of ECT systems in the application.展开更多
The task of dividing corrupted-data into their respective subspaces can be well illustrated,both theoretically and numerically,by recovering low-rank and sparse-column components of a given matrix.Generally,it can be ...The task of dividing corrupted-data into their respective subspaces can be well illustrated,both theoretically and numerically,by recovering low-rank and sparse-column components of a given matrix.Generally,it can be characterized as a matrix and a 2,1-norm involved convex minimization problem.However,solving the resulting problem is full of challenges due to the non-smoothness of the objective function.One of the earliest solvers is an 3-block alternating direction method of multipliers(ADMM)which updates each variable in a Gauss-Seidel manner.In this paper,we present three variants of ADMM for the 3-block separable minimization problem.More preciously,whenever one variable is derived,the resulting problems can be regarded as a convex minimization with 2 blocks,and can be solved immediately using the standard ADMM.If the inner iteration loops only once,the iterative scheme reduces to the ADMM with updates in a Gauss-Seidel manner.If the solution from the inner iteration is assumed to be exact,the convergence can be deduced easily in the literature.The performance comparisons with a couple of recently designed solvers illustrate that the proposed methods are effective and competitive.展开更多
This paper investigates the distributed model predictive control(MPC)problem of linear systems where the network topology is changeable by the way of inserting new subsystems,disconnecting existing subsystems,or merel...This paper investigates the distributed model predictive control(MPC)problem of linear systems where the network topology is changeable by the way of inserting new subsystems,disconnecting existing subsystems,or merely modifying the couplings between different subsystems.To equip live systems with a quick response ability when modifying network topology,while keeping a satisfactory dynamic performance,a novel reconfiguration control scheme based on the alternating direction method of multipliers(ADMM)is presented.In this scheme,the local controllers directly influenced by the structure realignment are redesigned in the reconfiguration control.Meanwhile,by employing the powerful ADMM algorithm,the iterative formulas for solving the reconfigured optimization problem are obtained,which significantly accelerate the computation speed and ensure a timely output of the reconfigured optimal control response.Ultimately,the presented reconfiguration scheme is applied to the level control of a benchmark four-tank plant to illustrate its effectiveness and main characteristics.展开更多
In this paper, a distributed algorithm is proposed to solve a kind of multi-objective optimization problem based on the alternating direction method of multipliers. Compared with the centralized algorithms, this algor...In this paper, a distributed algorithm is proposed to solve a kind of multi-objective optimization problem based on the alternating direction method of multipliers. Compared with the centralized algorithms, this algorithm does not need a central node. Therefore, it has the characteristics of low communication burden and high privacy. In addition, numerical experiments are provided to validate the effectiveness of the proposed algorithm.展开更多
Combined heat and power dispatch(CHPD)opens a new window for increasing operational flexibility and reducing wind power curtailment.Electric power and district heating systems are independently controlled by different...Combined heat and power dispatch(CHPD)opens a new window for increasing operational flexibility and reducing wind power curtailment.Electric power and district heating systems are independently controlled by different system operators;therefore,a decentralized solution paradigm is necessary for CHPD,in which only minor boundary information is required to be exchanged via a communication network.However,a nonideal communication environment with noise could lead to divergence or incorrect solutions of decentralized algorithms.To bridge this gap,this paper proposes a stochastic accelerated alternating direction method of multipliers(SA-ADMM)for hedging communication noise in CHPD.This algorithm provides a general framework to address more types of constraint sets and separable objective functions than the existing stochastic ADMM.Different from the single noise sources considered in the existing stochastic approximation methods,communication noise from multiple sources is addressed in both the local calculation and the variable update stages.Case studies of two test systems validate the effectiveness and robustness of the proposed SAADMM.展开更多
In this paper,we propose a new stopping criterion for Eckstein and Bertsekas’s generalized alternating direction method of multipliers.The stopping criterion is easy to verify,and the computational cost is much less ...In this paper,we propose a new stopping criterion for Eckstein and Bertsekas’s generalized alternating direction method of multipliers.The stopping criterion is easy to verify,and the computational cost is much less than the classical stopping criterion in the highly influential paper by Boyd et al.(Found Trends Mach Learn 3(1):1–122,2011).展开更多
Alternating direction method of multipliers(ADMM)receives much attention in the recent years due to various demands from machine learning and big data related optimization.In 2013,Ouyang et al.extend the ADMM to the s...Alternating direction method of multipliers(ADMM)receives much attention in the recent years due to various demands from machine learning and big data related optimization.In 2013,Ouyang et al.extend the ADMM to the stochastic setting for solving some stochastic optimization problems,inspired by the structural risk minimization principle.In this paper,we consider a stochastic variant of symmetric ADMM,named symmetric stochastic linearized ADMM(SSL-ADMM).In particular,using the framework of variational inequality,we analyze the convergence properties of SSL-ADMM.Moreover,we show that,with high probability,SSL-ADMM has O((ln N)·N^(-1/2))constraint violation bound and objective error bound for convex problems,and has O((ln N)^(2)·N^(-1))constraint violation bound and objective error bound for strongly convex problems,where N is the iteration number.Symmetric ADMM can improve the algorithmic performance compared to classical ADMM,numerical experiments for statistical machine learning show that such an improvement is also present in the stochastic setting.展开更多
The alternating direction method of multipliers(ADMM)is a benchmark for solving convex programming problems with separable objective functions and linear constraints.In the literature it has been illustrated as an app...The alternating direction method of multipliers(ADMM)is a benchmark for solving convex programming problems with separable objective functions and linear constraints.In the literature it has been illustrated as an application of the proximal point algorithm(PPA)to the dual problem of the model under consideration.This paper shows that ADMM can also be regarded as an application of PPA to the primal model with a customized choice of the proximal parameter.This primal illustration of ADMM is thus complemental to its dual illustration in the literature.This PPA revisit on ADMM from the primal perspective also enables us to recover the generalized ADMM proposed by Eckstein and Bertsekas easily.A worst-case O(1/t)convergence rate in ergodic sense is established for a slight extension of Eckstein and Bertsekas’s generalized ADMM.展开更多
This paper proposes a decentralized demand management approach to reduce the energy bill of industrial park and improve its economic gains.A demand management model for industrial park considering the integrated deman...This paper proposes a decentralized demand management approach to reduce the energy bill of industrial park and improve its economic gains.A demand management model for industrial park considering the integrated demand response of combined heat and power(CHP)units and thermal storage is firstly proposed.Specifically,by increasing the electricity outputs of CHP units during peak-load periods,not only the peak demand charge but also the energy charge can be reduced.The thermal storage can efficiently utilize the waste heat provided by CHP units and further increase the flexibility of CHP units.The heat dissipation of thermal storage,thermal delay effect,and heat losses of heat pipelines are considered for ensuring reliable solutions to the industrial park.The proposed model is formulated as a multi-period alternating current(AC)optimal power flow problem via the second-order conic programming formulation.The alternating direction method of multipliers(ADMM)algorithm is used to compute the proposed demand management model in a distributed manner,which can protect private data of all participants while achieving solutions with high quality.Numerical case studies validate the effectiveness of the proposed demand management approach in reducing peak demand charge,and the performance of the ADMM-based decentralized computation algorithm in deriving the same optimal results of demand management as the centralized approach is also validated.展开更多
Recently, alternating direction method of multipliers (ADMM) attracts much attentions from various fields and there are many variant versions tailored for differentmodels. Moreover, its theoretical studies such as rat...Recently, alternating direction method of multipliers (ADMM) attracts much attentions from various fields and there are many variant versions tailored for differentmodels. Moreover, its theoretical studies such as rate of convergence and extensionsto nonconvex problems also achieve much progress. In this paper, we give a surveyon some recent developments of ADMM and its variants.展开更多
We consider a wide range of non-convex regularized minimization problems, where the non-convex regularization term is composite with a linear function engaged in sparse learning. Recent theoretical investigations have...We consider a wide range of non-convex regularized minimization problems, where the non-convex regularization term is composite with a linear function engaged in sparse learning. Recent theoretical investigations have demonstrated their superiority over their convex counterparts. The computational challenge lies in the fact that the proximal mapping associated with non-convex regularization is not easily obtained due to the imposed linear composition. Fortunately, the problem structure allows one to introduce an auxiliary variable and reformulate it as an optimization problem with linear constraints, which can be solved using the Linearized Alternating Direction Method of Multipliers （LADMM）. Despite the success of LADMM in practice, it remains unknown whether LADMM is convergent in solving such non-convex compositely regularized optimizations. In this research, we first present a detailed convergence analysis of the LADMM algorithm for solving a non-convex compositely regularized optimization problem with a large class of non-convex penalties. Furthermore, we propose an Adaptive LADMM （AdaLADMM） algorithm with a line-search criterion. Experimental results on different genres of datasets validate the efficacy of the proposed algorithm.展开更多
Integrated electrical and heating systems(IEHSs)are promising for increasing the flexibility of power systems by exploiting the heat energy storage of pipelines.With the recent development of advanced communication te...Integrated electrical and heating systems(IEHSs)are promising for increasing the flexibility of power systems by exploiting the heat energy storage of pipelines.With the recent development of advanced communication technology,distributed optimization is employed in the coordination of IEHSs to meet the practical requirement of information privacy between different system operators.Existing studies on distributed optimization algorithms for IEHSs have seldom addressed packet loss during the process of information exchange.In this paper,a distributed paradigm is proposed for coordinating the operation of an IEHS considering communication packet loss.The relaxed alternating direction method of multipliers(R-ADMM)is derived by applying Peaceman-Rachford splitting to the Lagrangian dual of the primal problem.The proposed method is tested using several test systems in a lossy communication and transmission environment.Simulation results indicate the effectiveness and robustness of the proposed R-ADMM algorithm.展开更多
Symmetric alternating directionmethod of multipliers(ADMM)is an efficient method for solving a class of separable convex optimization problems.This method updates the Lagrange multiplier twice with appropriate step si...Symmetric alternating directionmethod of multipliers(ADMM)is an efficient method for solving a class of separable convex optimization problems.This method updates the Lagrange multiplier twice with appropriate step sizes at each iteration.However,such step sizes were conservatively shrunk to guarantee the convergence in recent studies.In this paper,we are devoted to seeking larger step sizes whenever possible.The logarithmic-quadratic proximal(LQP)terms are applied to regularize the symmetric ADMM subproblems,allowing the constrained subproblems to then be converted to easier unconstrained ones.Theoretically,we prove the global convergence of such LQP-based symmetric ADMM by specifying a larger step size domain.Moreover,the numerical results on a traffic equilibrium problem are reported to demonstrate the advantage of the method with larger step sizes.展开更多
The minimax concave penalty （MCP） has been demonstrated theoretically and practical- ly to be effective in nonconvex penalization for variable selection and parameter estimation. In this paper, we develop an efficie...The minimax concave penalty （MCP） has been demonstrated theoretically and practical- ly to be effective in nonconvex penalization for variable selection and parameter estimation. In this paper, we develop an efficient alternating direction method of multipliers （ADMM） with continuation algorithm for solving the MCP-penalized least squares problem in high dimensions. Under some mild conditions, we study the convergence properties and the Karush-Kuhn-Tucker （KKT） optimality con- ditions of the proposed method. A high-dimensional BIC is developed to select the optimal tuning parameters. Simulations and a real data example are presented to illustrate the efficiency and accuracy of the proposed method.展开更多
In recent years,alternating direction method of multipliers(ADMM)and its variants are popular for the extensive use in image processing and statistical learning.A variant of ADMM:symmetric ADMM,which updates the Lagra...In recent years,alternating direction method of multipliers(ADMM)and its variants are popular for the extensive use in image processing and statistical learning.A variant of ADMM:symmetric ADMM,which updates the Lagrange mul-tiplier twice in one iteration,is always faster whenever it converges.In this paper,combined with Nesterov’s accelerating strategy,an accelerated symmetric ADMM is proposed.We prove its O(1/k^(2))convergence rate under strongly convex condition.For the general situation,an accelerated method with a restart rule is proposed.Some preliminary numerical experiments show the efficiency of our algorithms.展开更多
Linearly constrained separable convex minimization problems have been raised widely in many real-world applications.In this paper,we propose a homotopy-based alternating direction method of multipliers for solving thi...Linearly constrained separable convex minimization problems have been raised widely in many real-world applications.In this paper,we propose a homotopy-based alternating direction method of multipliers for solving this kind of problems.The proposed method owns some advantages of the classical proximal alternating direction method of multipliers and homotopy method.Under some suitable condi-tions,we prove global convergence and the worst-case O(k/1)convergence rate in a nonergodic sense.Preliminary numerical results indicate effectiveness and efficiency of the proposed method compared with some state-of-the-art methods.展开更多
Linear programming is the core problem of various operational research problems.The dominant approaches for linear programming are simplex and interior point methods.In this paper,we showthat the alternating direction...Linear programming is the core problem of various operational research problems.The dominant approaches for linear programming are simplex and interior point methods.In this paper,we showthat the alternating direction method of multipliers(ADMM),which was proposed long time ago while recently found more and more applications in a broad spectrum of areas,can also be easily used to solve the canonical linear programming model.The resulting per-iteration complexity is O(mn)where m is the constraint number and n the variable dimension.At each iteration,there are m subproblems that are eligible for parallel computation;each requiring only O(n)flops.There is no inner iteration as well.We thus introduce the newADMMapproach to linear programming,which may inspire deeper research for more complicated scenarios with more sophisticated results.展开更多
In recent years,there has been a growing usage of sparse representations in signal processing.This paper revisits theK-SVD,an algorithm for designing overcomplete dictionaries for sparse and redundant representations....In recent years,there has been a growing usage of sparse representations in signal processing.This paper revisits theK-SVD,an algorithm for designing overcomplete dictionaries for sparse and redundant representations.We present a newapproach to solve dictionary learning models by combining the alternating direction method of multipliers and the orthogonal matching pursuit.The experimental results show that our approach can reliably obtain better learned dictionary elements and outperform other algorithms.展开更多
A new algorithm,called symmetric inertial alternating direction method of multipliers(SIADMM),is designed for separable convex optimization problems with linear constraints in this paper.The convergence rate of the SI...A new algorithm,called symmetric inertial alternating direction method of multipliers(SIADMM),is designed for separable convex optimization problems with linear constraints in this paper.The convergence rate of the SIADMM is proved to be O(1/√k).Two kinds of elliptic equation constrained optimization problems,the un-constrained cases as well as the box-constrained cases of the distributed control and the Robin boundary control,are analyzed theoretically and solved numerically.First,the existence and uniqueness of the solutions to these problems are proved.Second,these continuous optimization problems are transformed into discrete optimization problems by thefinite element method,and then the discrete optimization problems are solved by the proposed SIADMM.Numerical experiments with different problems are investigated to demonstrate the efficiency of the SIADMM.And the numerical per-formance of the SIADMM is better than the performance of the ADMM.Moreover,the numerical results show that the convergence rate of the SIADMM tends to be faster than O(1/√k)in calculation process.展开更多
In this paper,we propose an image denoising algorithm for compressed sensing based on alternating direction method of multipliers(ADMM).We prove that the objective func-tion of the iterates approaches the optimal valu...In this paper,we propose an image denoising algorithm for compressed sensing based on alternating direction method of multipliers(ADMM).We prove that the objective func-tion of the iterates approaches the optimal value.We also prove the O(1/N)convergence rate of our algorithm in the ergodic sense.At the same time,simulation results show that our algorithm is more efficient in image denoising compared with existing methods.展开更多
基金Supported by the National Natural Science Foundation of China(61203021)the Key Science and Technology Program of Liaoning Province(2011216011)+1 种基金the Natural Science Foundation of Liaoning Province(2013020024)the Program for Liaoning Excellent Talents in Universities(LJQ2015061)
文摘Electrical capacitance tomography(ECT)has been applied to two-phase flow measurement in recent years.Image reconstruction algorithms play an important role in the successful applications of ECT.To solve the ill-posed and nonlinear inverse problem of ECT image reconstruction,a new ECT image reconstruction method based on fast linearized alternating direction method of multipliers(FLADMM)is proposed in this paper.On the basis of theoretical analysis of compressed sensing(CS),the data acquisition of ECT is regarded as a linear measurement process of permittivity distribution signal of pipe section.A new measurement matrix is designed and L1 regularization method is used to convert ECT inverse problem to a convex relaxation problem which contains prior knowledge.A new fast alternating direction method of multipliers which contained linearized idea is employed to minimize the objective function.Simulation data and experimental results indicate that compared with other methods,the quality and speed of reconstructed images are markedly improved.Also,the dynamic experimental results indicate that the proposed algorithm can ful fill the real-time requirement of ECT systems in the application.
基金Supported by the National Natural Science Foundation of China(Grant No.11971149,11871381)Natural Science Foundation of Henan Province for Youth(Grant No.202300410146)。
文摘The task of dividing corrupted-data into their respective subspaces can be well illustrated,both theoretically and numerically,by recovering low-rank and sparse-column components of a given matrix.Generally,it can be characterized as a matrix and a 2,1-norm involved convex minimization problem.However,solving the resulting problem is full of challenges due to the non-smoothness of the objective function.One of the earliest solvers is an 3-block alternating direction method of multipliers(ADMM)which updates each variable in a Gauss-Seidel manner.In this paper,we present three variants of ADMM for the 3-block separable minimization problem.More preciously,whenever one variable is derived,the resulting problems can be regarded as a convex minimization with 2 blocks,and can be solved immediately using the standard ADMM.If the inner iteration loops only once,the iterative scheme reduces to the ADMM with updates in a Gauss-Seidel manner.If the solution from the inner iteration is assumed to be exact,the convergence can be deduced easily in the literature.The performance comparisons with a couple of recently designed solvers illustrate that the proposed methods are effective and competitive.
基金the National Natural Science Foundation of China(61833012,61773162,61590924)the Natural Science Foundation of Shanghai(18ZR1420000)。
文摘This paper investigates the distributed model predictive control(MPC)problem of linear systems where the network topology is changeable by the way of inserting new subsystems,disconnecting existing subsystems,or merely modifying the couplings between different subsystems.To equip live systems with a quick response ability when modifying network topology,while keeping a satisfactory dynamic performance,a novel reconfiguration control scheme based on the alternating direction method of multipliers(ADMM)is presented.In this scheme,the local controllers directly influenced by the structure realignment are redesigned in the reconfiguration control.Meanwhile,by employing the powerful ADMM algorithm,the iterative formulas for solving the reconfigured optimization problem are obtained,which significantly accelerate the computation speed and ensure a timely output of the reconfigured optimal control response.Ultimately,the presented reconfiguration scheme is applied to the level control of a benchmark four-tank plant to illustrate its effectiveness and main characteristics.
文摘In this paper, a distributed algorithm is proposed to solve a kind of multi-objective optimization problem based on the alternating direction method of multipliers. Compared with the centralized algorithms, this algorithm does not need a central node. Therefore, it has the characteristics of low communication burden and high privacy. In addition, numerical experiments are provided to validate the effectiveness of the proposed algorithm.
基金supported by the Key-Area Research and Development Program of Guangdong Province under Grant 2020B010166004the National Natural Science Foundation of China under Grant 52177086+2 种基金the Guangdong Basic and Applied Basic Research Foundation under Grant 2019A1515011408the Science and Technology Program of Guangzhou under Grant 201904010215the Talent Recruitment Project of Guangdong under Grant 2017GC010467.
文摘Combined heat and power dispatch(CHPD)opens a new window for increasing operational flexibility and reducing wind power curtailment.Electric power and district heating systems are independently controlled by different system operators;therefore,a decentralized solution paradigm is necessary for CHPD,in which only minor boundary information is required to be exchanged via a communication network.However,a nonideal communication environment with noise could lead to divergence or incorrect solutions of decentralized algorithms.To bridge this gap,this paper proposes a stochastic accelerated alternating direction method of multipliers(SA-ADMM)for hedging communication noise in CHPD.This algorithm provides a general framework to address more types of constraint sets and separable objective functions than the existing stochastic ADMM.Different from the single noise sources considered in the existing stochastic approximation methods,communication noise from multiple sources is addressed in both the local calculation and the variable update stages.Case studies of two test systems validate the effectiveness and robustness of the proposed SAADMM.
文摘In this paper,we propose a new stopping criterion for Eckstein and Bertsekas’s generalized alternating direction method of multipliers.The stopping criterion is easy to verify,and the computational cost is much less than the classical stopping criterion in the highly influential paper by Boyd et al.(Found Trends Mach Learn 3(1):1–122,2011).
基金Supported by National Natural Science Foundation of China (61662036)。
文摘Alternating direction method of multipliers(ADMM)receives much attention in the recent years due to various demands from machine learning and big data related optimization.In 2013,Ouyang et al.extend the ADMM to the stochastic setting for solving some stochastic optimization problems,inspired by the structural risk minimization principle.In this paper,we consider a stochastic variant of symmetric ADMM,named symmetric stochastic linearized ADMM(SSL-ADMM).In particular,using the framework of variational inequality,we analyze the convergence properties of SSL-ADMM.Moreover,we show that,with high probability,SSL-ADMM has O((ln N)·N^(-1/2))constraint violation bound and objective error bound for convex problems,and has O((ln N)^(2)·N^(-1))constraint violation bound and objective error bound for strongly convex problems,where N is the iteration number.Symmetric ADMM can improve the algorithmic performance compared to classical ADMM,numerical experiments for statistical machine learning show that such an improvement is also present in the stochastic setting.
基金supported by National Natural Science Foundation of China(Grant Nos.11001124 and 91130007)the Doctoral Fund of Ministry of Eduction of China(Grant No.20110091110004)the General Research Fund from Hong Kong Research Grants Council(Grant No.HKBU 203712)
文摘The alternating direction method of multipliers(ADMM)is a benchmark for solving convex programming problems with separable objective functions and linear constraints.In the literature it has been illustrated as an application of the proximal point algorithm(PPA)to the dual problem of the model under consideration.This paper shows that ADMM can also be regarded as an application of PPA to the primal model with a customized choice of the proximal parameter.This primal illustration of ADMM is thus complemental to its dual illustration in the literature.This PPA revisit on ADMM from the primal perspective also enables us to recover the generalized ADMM proposed by Eckstein and Bertsekas easily.A worst-case O(1/t)convergence rate in ergodic sense is established for a slight extension of Eckstein and Bertsekas’s generalized ADMM.
基金This work was supported by the National Key R&D Program of China(No.2018YFB0905000)the Science and Technology Project of State Grid Corporation of China(No.SGTJDK00DWJS1800232).
文摘This paper proposes a decentralized demand management approach to reduce the energy bill of industrial park and improve its economic gains.A demand management model for industrial park considering the integrated demand response of combined heat and power(CHP)units and thermal storage is firstly proposed.Specifically,by increasing the electricity outputs of CHP units during peak-load periods,not only the peak demand charge but also the energy charge can be reduced.The thermal storage can efficiently utilize the waste heat provided by CHP units and further increase the flexibility of CHP units.The heat dissipation of thermal storage,thermal delay effect,and heat losses of heat pipelines are considered for ensuring reliable solutions to the industrial park.The proposed model is formulated as a multi-period alternating current(AC)optimal power flow problem via the second-order conic programming formulation.The alternating direction method of multipliers(ADMM)algorithm is used to compute the proposed demand management model in a distributed manner,which can protect private data of all participants while achieving solutions with high quality.Numerical case studies validate the effectiveness of the proposed demand management approach in reducing peak demand charge,and the performance of the ADMM-based decentralized computation algorithm in deriving the same optimal results of demand management as the centralized approach is also validated.
基金This work is supported by the National Natural Science Foundation of China(Nos.11625105 and 12131004).
文摘Recently, alternating direction method of multipliers (ADMM) attracts much attentions from various fields and there are many variant versions tailored for differentmodels. Moreover, its theoretical studies such as rate of convergence and extensionsto nonconvex problems also achieve much progress. In this paper, we give a surveyon some recent developments of ADMM and its variants.
基金supported by the National Natural Science Foundation of China(Nos.61303264,61202482,and 61202488)Guangxi Cooperative Innovation Center of Cloud Computing and Big Data(No.YD16505)Distinguished Young Scientist Promotion of National University of Defense Technology
文摘We consider a wide range of non-convex regularized minimization problems, where the non-convex regularization term is composite with a linear function engaged in sparse learning. Recent theoretical investigations have demonstrated their superiority over their convex counterparts. The computational challenge lies in the fact that the proximal mapping associated with non-convex regularization is not easily obtained due to the imposed linear composition. Fortunately, the problem structure allows one to introduce an auxiliary variable and reformulate it as an optimization problem with linear constraints, which can be solved using the Linearized Alternating Direction Method of Multipliers （LADMM）. Despite the success of LADMM in practice, it remains unknown whether LADMM is convergent in solving such non-convex compositely regularized optimizations. In this research, we first present a detailed convergence analysis of the LADMM algorithm for solving a non-convex compositely regularized optimization problem with a large class of non-convex penalties. Furthermore, we propose an Adaptive LADMM （AdaLADMM） algorithm with a line-search criterion. Experimental results on different genres of datasets validate the efficacy of the proposed algorithm.
基金supported in part by the Key-Area Research and Development Program of Guangdong Province(No.2020B010166004)the Guangdong Basic and Applied Basic Research Foundation(No.2019A1515011408)+1 种基金the Talent Recruitment Project of Guangdong(No.2017GC010467)the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources,North China Electric Power University(No.LAPS19011)
文摘Integrated electrical and heating systems(IEHSs)are promising for increasing the flexibility of power systems by exploiting the heat energy storage of pipelines.With the recent development of advanced communication technology,distributed optimization is employed in the coordination of IEHSs to meet the practical requirement of information privacy between different system operators.Existing studies on distributed optimization algorithms for IEHSs have seldom addressed packet loss during the process of information exchange.In this paper,a distributed paradigm is proposed for coordinating the operation of an IEHS considering communication packet loss.The relaxed alternating direction method of multipliers(R-ADMM)is derived by applying Peaceman-Rachford splitting to the Lagrangian dual of the primal problem.The proposed method is tested using several test systems in a lossy communication and transmission environment.Simulation results indicate the effectiveness and robustness of the proposed R-ADMM algorithm.
基金This research was supported by National Natural Science Foundation of China Grant 11771078Natural Science Foundation of Jiangsu Province Grant BK20181258+1 种基金Project of 333 of Jiangsu Province Grant BRA2018351Postgraduate Research&Practice Innovation Program of Jiangsu Province Grant KYCX18_0200.
文摘Symmetric alternating directionmethod of multipliers(ADMM)is an efficient method for solving a class of separable convex optimization problems.This method updates the Lagrange multiplier twice with appropriate step sizes at each iteration.However,such step sizes were conservatively shrunk to guarantee the convergence in recent studies.In this paper,we are devoted to seeking larger step sizes whenever possible.The logarithmic-quadratic proximal(LQP)terms are applied to regularize the symmetric ADMM subproblems,allowing the constrained subproblems to then be converted to easier unconstrained ones.Theoretically,we prove the global convergence of such LQP-based symmetric ADMM by specifying a larger step size domain.Moreover,the numerical results on a traffic equilibrium problem are reported to demonstrate the advantage of the method with larger step sizes.
基金Supported by the National Natural Science Foundation of China(Grant Nos.11571263,11501579,11701571 and41572315)the Fundamental Research Funds for the Central Universities,China University of Geosciences(Wuhan)(Grant No.CUGW150809)
文摘The minimax concave penalty （MCP） has been demonstrated theoretically and practical- ly to be effective in nonconvex penalization for variable selection and parameter estimation. In this paper, we develop an efficient alternating direction method of multipliers （ADMM） with continuation algorithm for solving the MCP-penalized least squares problem in high dimensions. Under some mild conditions, we study the convergence properties and the Karush-Kuhn-Tucker （KKT） optimality con- ditions of the proposed method. A high-dimensional BIC is developed to select the optimal tuning parameters. Simulations and a real data example are presented to illustrate the efficiency and accuracy of the proposed method.
基金This research is partly supported by the National Natural Sci-ence Foundation of China(Grant No.11671217)Natural Science Foundation of Xinjiang(Grant No.2017D01A14)。
文摘In recent years,alternating direction method of multipliers(ADMM)and its variants are popular for the extensive use in image processing and statistical learning.A variant of ADMM:symmetric ADMM,which updates the Lagrange mul-tiplier twice in one iteration,is always faster whenever it converges.In this paper,combined with Nesterov’s accelerating strategy,an accelerated symmetric ADMM is proposed.We prove its O(1/k^(2))convergence rate under strongly convex condition.For the general situation,an accelerated method with a restart rule is proposed.Some preliminary numerical experiments show the efficiency of our algorithms.
基金the National Natural Science Foundation of China(Nos.11571074 and 61672005)the Natural Science Foundation of Fujian Province(No.2015J01010).
文摘Linearly constrained separable convex minimization problems have been raised widely in many real-world applications.In this paper,we propose a homotopy-based alternating direction method of multipliers for solving this kind of problems.The proposed method owns some advantages of the classical proximal alternating direction method of multipliers and homotopy method.Under some suitable condi-tions,we prove global convergence and the worst-case O(k/1)convergence rate in a nonergodic sense.Preliminary numerical results indicate effectiveness and efficiency of the proposed method compared with some state-of-the-art methods.
基金Bing-Sheng He was supported by the National Natural Science Foundation of China(No.11471156)Xiao-Ming Yuan was supported by the General Research Fund from Hong Kong Research Grants Council(No.12302514).
文摘Linear programming is the core problem of various operational research problems.The dominant approaches for linear programming are simplex and interior point methods.In this paper,we showthat the alternating direction method of multipliers(ADMM),which was proposed long time ago while recently found more and more applications in a broad spectrum of areas,can also be easily used to solve the canonical linear programming model.The resulting per-iteration complexity is O(mn)where m is the constraint number and n the variable dimension.At each iteration,there are m subproblems that are eligible for parallel computation;each requiring only O(n)flops.There is no inner iteration as well.We thus introduce the newADMMapproach to linear programming,which may inspire deeper research for more complicated scenarios with more sophisticated results.
文摘In recent years,there has been a growing usage of sparse representations in signal processing.This paper revisits theK-SVD,an algorithm for designing overcomplete dictionaries for sparse and redundant representations.We present a newapproach to solve dictionary learning models by combining the alternating direction method of multipliers and the orthogonal matching pursuit.The experimental results show that our approach can reliably obtain better learned dictionary elements and outperform other algorithms.
基金This work was supported by National Natural Science Foundation of China(Grant Nos.12171052,11871115 and 11671052)BUPT Excellent Ph.D.Students Foundation(Grant No.CX2021320).The authors sincerely thank Prof.Haiming Song and Doctor Xin Gao for their valuable discussions.The authors also thank all of the editors and reviewers for their very important suggestions.
文摘A new algorithm,called symmetric inertial alternating direction method of multipliers(SIADMM),is designed for separable convex optimization problems with linear constraints in this paper.The convergence rate of the SIADMM is proved to be O(1/√k).Two kinds of elliptic equation constrained optimization problems,the un-constrained cases as well as the box-constrained cases of the distributed control and the Robin boundary control,are analyzed theoretically and solved numerically.First,the existence and uniqueness of the solutions to these problems are proved.Second,these continuous optimization problems are transformed into discrete optimization problems by thefinite element method,and then the discrete optimization problems are solved by the proposed SIADMM.Numerical experiments with different problems are investigated to demonstrate the efficiency of the SIADMM.And the numerical per-formance of the SIADMM is better than the performance of the ADMM.Moreover,the numerical results show that the convergence rate of the SIADMM tends to be faster than O(1/√k)in calculation process.
基金This work is partially supported by the National Natural Science Foundation of China(No.11771350)Basic and Advanced Research Project of CQ CSTC(Nos.cstc2020jcyj-msxmX0738 and cstc2018jcyjAX0605).
文摘In this paper,we propose an image denoising algorithm for compressed sensing based on alternating direction method of multipliers(ADMM).We prove that the objective func-tion of the iterates approaches the optimal value.We also prove the O(1/N)convergence rate of our algorithm in the ergodic sense.At the same time,simulation results show that our algorithm is more efficient in image denoising compared with existing methods.
