In this paper, a class of generalized strongly nonlinear quasivariational inclusions are studied. By using the properties of the resolvent operator associated with a maximal monotone; mapping in Hilbert space, an exis...In this paper, a class of generalized strongly nonlinear quasivariational inclusions are studied. By using the properties of the resolvent operator associated with a maximal monotone; mapping in Hilbert space, an existence theorem of solutions for generalized strongly nonlinear quasivariational inclusion is established and a new proximal point algorithm with errors is suggested for finding approximate solutions which strongly converge to the exact solution of the generalized strongly, nonlinear quasivariational inclusion. As special cases, some known results in this field are also discussed.展开更多
In order to find roots of maximal monotone operators, this paper introduces and studies the modified approximate proximal point algorithm with an error sequence {e k} such that || ek || \leqslant hk || xk - [(x)\tilde...In order to find roots of maximal monotone operators, this paper introduces and studies the modified approximate proximal point algorithm with an error sequence {e k} such that || ek || \leqslant hk || xk - [(x)\tilde]k ||\left\| { e^k } \right\| \leqslant \eta _k \left\| { x^k - \tilde x^k } \right\| with ?k = 0¥ ( hk - 1 ) < + ¥\sum\limits_{k = 0}^\infty {\left( {\eta _k - 1} \right)} and infk \geqslant 0 hk = m\geqslant 1\mathop {\inf }\limits_{k \geqslant 0} \eta _k = \mu \geqslant 1 . Here, the restrictions on {η k} are very different from the ones on {η k}, given by He et al (Science in China Ser. A, 2002, 32 (11): 1026–1032.) that supk \geqslant 0 hk = v < 1\mathop {\sup }\limits_{k \geqslant 0} \eta _k = v . Moreover, the characteristic conditions of the convergence of the modified approximate proximal point algorithm are presented by virtue of the new technique very different from the ones given by He et al.展开更多
Proximal point algorithm(PPA)is a useful algorithm framework and has good convergence properties.Themain difficulty is that the subproblems usually only have iterative solutions.In this paper,we propose an inexact cus...Proximal point algorithm(PPA)is a useful algorithm framework and has good convergence properties.Themain difficulty is that the subproblems usually only have iterative solutions.In this paper,we propose an inexact customized PPA framework for twoblock separable convex optimization problem with linear constraint.We design two types of inexact error criteria for the subproblems.The first one is absolutely summable error criterion,under which both subproblems can be solved inexactly.When one of the two subproblems is easily solved,we propose another novel error criterion which is easier to implement,namely relative error criterion.The relative error criterion only involves one parameter,which is more implementable.We establish the global convergence and sub-linear convergence rate in ergodic sense for the proposed algorithms.The numerical experiments on LASSO regression problems and total variation-based image denoising problem illustrate that our new algorithms outperform the corresponding exact algorithms.展开更多
In this paper we present some algorithms for minimization of DC function (difference of two convex functions). They are descent methods of the proximal-type which use the convex properties of the two convex functions ...In this paper we present some algorithms for minimization of DC function (difference of two convex functions). They are descent methods of the proximal-type which use the convex properties of the two convex functions separately. We also consider an approximate proximal point algorithm. Some properties of the ε-subdifferential and the ε-directional derivative are discussed. The convergence properties of the algorithms are established in both exact and approximate forms. Finally, we give some applications to the concave programming and maximum eigenvalue problems.展开更多
In this paper,we present an analysis about the rate of convergence of an inexact proximal point algorithm to solve minimization problems for quasiconvex objective functions on Hadamard manifolds.We prove that under na...In this paper,we present an analysis about the rate of convergence of an inexact proximal point algorithm to solve minimization problems for quasiconvex objective functions on Hadamard manifolds.We prove that under natural assumptions the sequence generated by the algorithm converges linearly or superlinearly to a critical point of the problem.展开更多
The problem of finding a zero point of a maximal monotone operator plays a central role in modeling many application problems arising from various fields,and the proximal point algorithm(PPA)is among the fundamental a...The problem of finding a zero point of a maximal monotone operator plays a central role in modeling many application problems arising from various fields,and the proximal point algorithm(PPA)is among the fundamental algorithms for solving the zero-finding problem.PPA not only provides a very general framework of analyzing convergence and rate of convergence of many algorithms,but also can be very efficient in solving some structured problems.In this paper,we give a survey on the developments of PPA and its variants,including the recent results with linear proximal term,with the nonlinear proximal term,as well as the inexact forms with various approximate criteria.展开更多
In this paper we present two inexact proximal point algorithms to solve minimization problems for quasiconvex objective functions on Hadamard manifolds.We prove that under natural assumptions the sequence generated by...In this paper we present two inexact proximal point algorithms to solve minimization problems for quasiconvex objective functions on Hadamard manifolds.We prove that under natural assumptions the sequence generated by the algorithms are well defined and converge to critical points of the problem.We also present an application of the method to demand theory in economy.展开更多
The proximal point algorithm has many interesting applications,such as signal recovery,signal processing and others.In recent years,the proximal point method has been extended to Riemannian manifolds.The main advantag...The proximal point algorithm has many interesting applications,such as signal recovery,signal processing and others.In recent years,the proximal point method has been extended to Riemannian manifolds.The main advantages of these extensions are that nonconvex problems in classic sense may become geodesic convex by introducing an appropriate Riemannian metric,constrained optimization problems may be seen as unconstrained ones.In this paper,we propose an inexact proximal point algorithm for geodesic convex vector function on Hadamard manifolds.Under the assumption that the objective function is coercive,the sequence generated by this algorithm converges to a Pareto critical point.When the objective function is coercive and strictly geodesic convex,the sequence generated by this algorithm converges to a Pareto optimal point.Furthermore,under the weaker growth condition,we prove that the inexact proximal point algorithm has linear/superlinear convergence rate.展开更多
We study the existence of best proximity points for single-valued non-self map-pings. Also, we prove a best proximity point theorem for set-valued non-self mappings in metric spaces with an appropriate geometric prope...We study the existence of best proximity points for single-valued non-self map-pings. Also, we prove a best proximity point theorem for set-valued non-self mappings in metric spaces with an appropriate geometric property. Examples are given to support the usability of our results.展开更多
A unified efficient algorithm framework of proximal-based decomposition methods has been proposed for monotone variational inequalities in 2012,while only global convergence is proved at the same time.In this paper,we...A unified efficient algorithm framework of proximal-based decomposition methods has been proposed for monotone variational inequalities in 2012,while only global convergence is proved at the same time.In this paper,we give a unified proof on theO(1/t)iteration complexity,together with the linear convergence rate for this kind of proximal-based decomposition methods.Besides theε-optimal iteration complexity result defined by variational inequality,the non-ergodic relative error of adjacent iteration points is also proved to decrease in the same order.Further,the linear convergence rate of this algorithm framework can be constructed based on some special variational inequality properties,without necessary strong monotone conditions.展开更多
We present an extension of the proximal point method with Bregman distances to solve variational inequality problems(VIP)on Hadamard manifolds with null sectional curvature.Under some natural assumptions,as for exampl...We present an extension of the proximal point method with Bregman distances to solve variational inequality problems(VIP)on Hadamard manifolds with null sectional curvature.Under some natural assumptions,as for example,the existence of solutions of the VIP and the monotonicity of the multivalued vector field,we prove that the sequence of the iterates given by the method converges to a solution of the problem.Furthermore,this convergence is linear or superlinear with respect to the Bregman distance.展开更多
This paper presents two proximal-based pre-correction decomposition methods for convex minimization problems with separable structures.The methods,derived from Chen and Teboulle’s proximal-based decomposition method ...This paper presents two proximal-based pre-correction decomposition methods for convex minimization problems with separable structures.The methods,derived from Chen and Teboulle’s proximal-based decomposition method and He’s parallel splitting augmented Lagrangian method,remain the nice convergence property of the proximal point method and could compute variables in parallel like He’s method under the prediction-correction framework.Convergence results are established without additional assumptions.And the efficiency of the proposed methods is illustrated by some preliminary numerical experiments.展开更多
In this paper,we propose a fast proximity point algorithm and apply it to total variation(TV)based image restoration.The novel method is derived from the idea of establishing a general proximity point operator framewo...In this paper,we propose a fast proximity point algorithm and apply it to total variation(TV)based image restoration.The novel method is derived from the idea of establishing a general proximity point operator framework based on which new first-order schemes for total variation(TV)based image restoration have been proposed.Many current algorithms for TV-based image restoration,such as Chambolle’s projection algorithm,the split Bregman algorithm,the Berm´udez-Moreno algorithm,the Jia-Zhao denoising algorithm,and the fixed point algorithm,can be viewed as special cases of the new first-order schemes.Moreover,the convergence of the new algorithm has been analyzed at length.Finally,we make comparisons with the split Bregman algorithm which is one of the best algorithms for solving TV-based image restoration at present.Numerical experiments illustrate the efficiency of the proposed algorithms.展开更多
We generalize Ekeland's Variational Principle for cyclic maps. We present applications of this version of the variational principle for proving of existence and uniqueness of best proximity points for different class...We generalize Ekeland's Variational Principle for cyclic maps. We present applications of this version of the variational principle for proving of existence and uniqueness of best proximity points for different classes of cyclic maps.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
In this paper, we focus on the real-time interactions among multiple utility companies and multiple users and formulate real-time pricing(RTP) as a two-stage optimization problem. At the first stage, based on cost fun...In this paper, we focus on the real-time interactions among multiple utility companies and multiple users and formulate real-time pricing(RTP) as a two-stage optimization problem. At the first stage, based on cost function, we propose a continuous supply function bidding mechanism to model the utility companies’ profit maximization problem, by which the analytic expression of electricity price is further derived. At the second stage, considering that individually optimal solution may not be socially optimal, we employ convex optimization with linear constraints to model the price anticipating users’ daily payoff maximum. Substitute the analytic expression of electricity price obtained at the first stage into the optimization problem at the second stage. Using customized proximal point algorithm(C-PPA), the optimization problem at the second stage is solved and electricity price is obtained accordingly. We also prove the existence and uniqueness of the Nash equilibrium in the mentioned twostage optimization and the convergence of C-PPA. In addition, in order to make the algorithm more practical, a statistical approach is used to obtain the function of price only through online information exchange, instead of solving it directly. The proposed approach offers RTP, power production and load scheduling for multiple utility companies and multiple users in smart grid. Statistical approach helps to protect the company’s privacy and avoid the interference of random factors, and C-PPA has an advantage over Lagrangian algorithm because the former need not obtain the objection function of the dual optimization problem by solving an optimization problem with parameters. Simulation results show that the proposed framework can significantly reduce peak time loading and efficiently balance system energy distribution.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
Linearly constrained convex optimization has many applications.The first-order optimal condition of the linearly constrained convex optimization is a monotone variational inequality(VI).For solving VI,the proximal poi...Linearly constrained convex optimization has many applications.The first-order optimal condition of the linearly constrained convex optimization is a monotone variational inequality(VI).For solving VI,the proximal point algorithm(PPA)in Euclideannorm is classical but abstract.Hence,the classical PPA only plays an important theoretical role and it is rarely used in the practical scientific computation.In this paper,we give a review on the recently developed customized PPA in Hnorm(H is a positive definite matrix).In the frame of customized PPA,it is easy to construct the contraction-type methods for convex optimization with different linear constraints.In each iteration of the proposed methods,we need only to solve the proximal subproblems which have the closed form solutions or can be efficiently solved up to a high precision.Some novel applications and numerical experiments are reported.Additionally,the original primaldual hybrid gradient method is modified to a convergent algorithm by using a prediction-correction uniform framework.Using the variational inequality approach,the contractive convergence and convergence rate proofs of the framework are more general and quite simple.展开更多
In this paper, we prove a strong convergence theorem for resolvents of accretive operators in a Banach space by the viscosity approximation method with a generalized contraction mapping. The proximal point algorithm i...In this paper, we prove a strong convergence theorem for resolvents of accretive operators in a Banach space by the viscosity approximation method with a generalized contraction mapping. The proximal point algorithm in a Banach space is also considered. The results extend some very recent theorems of W. Takahashi.展开更多
Sparse signal recovery is a topic of considerable interest,and the literature in this field is already quite immense.Many problems that arise in sparse signal recovery can be generalized as a convex programming with l...Sparse signal recovery is a topic of considerable interest,and the literature in this field is already quite immense.Many problems that arise in sparse signal recovery can be generalized as a convex programming with linear conic constraints.In this paper,we present a new proximal point algorithm(PPA) termed as relaxed-PPA(RPPA) contraction method,for solving this common convex programming.More precisely,we first reformulate the convex programming into an equivalent variational inequality(VI),and then efficiently explore its inner structure.In each step,our method relaxes the VI-subproblem to a tractable one,which can be solved much more efficiently than the original VI.Under mild conditions,the convergence of the proposed method is proved.Experiments with l1 analysis show that RPPA is a computationally efficient algorithm and compares favorably with the recently proposed state-of-the-art algorithms.展开更多
文摘In this paper, a class of generalized strongly nonlinear quasivariational inclusions are studied. By using the properties of the resolvent operator associated with a maximal monotone; mapping in Hilbert space, an existence theorem of solutions for generalized strongly nonlinear quasivariational inclusion is established and a new proximal point algorithm with errors is suggested for finding approximate solutions which strongly converge to the exact solution of the generalized strongly, nonlinear quasivariational inclusion. As special cases, some known results in this field are also discussed.
基金Supported both by the Teaching and Research Award Fund for Outstanding Young Teachers inHigher Educational Institutions of MOEChinaand by the Dawn Program Fund in Shanghai
文摘In order to find roots of maximal monotone operators, this paper introduces and studies the modified approximate proximal point algorithm with an error sequence {e k} such that || ek || \leqslant hk || xk - [(x)\tilde]k ||\left\| { e^k } \right\| \leqslant \eta _k \left\| { x^k - \tilde x^k } \right\| with ?k = 0¥ ( hk - 1 ) < + ¥\sum\limits_{k = 0}^\infty {\left( {\eta _k - 1} \right)} and infk \geqslant 0 hk = m\geqslant 1\mathop {\inf }\limits_{k \geqslant 0} \eta _k = \mu \geqslant 1 . Here, the restrictions on {η k} are very different from the ones on {η k}, given by He et al (Science in China Ser. A, 2002, 32 (11): 1026–1032.) that supk \geqslant 0 hk = v < 1\mathop {\sup }\limits_{k \geqslant 0} \eta _k = v . Moreover, the characteristic conditions of the convergence of the modified approximate proximal point algorithm are presented by virtue of the new technique very different from the ones given by He et al.
基金the National Natural Science Foundation of China(Nos.11971238 and 11871279)。
文摘Proximal point algorithm(PPA)is a useful algorithm framework and has good convergence properties.Themain difficulty is that the subproblems usually only have iterative solutions.In this paper,we propose an inexact customized PPA framework for twoblock separable convex optimization problem with linear constraint.We design two types of inexact error criteria for the subproblems.The first one is absolutely summable error criterion,under which both subproblems can be solved inexactly.When one of the two subproblems is easily solved,we propose another novel error criterion which is easier to implement,namely relative error criterion.The relative error criterion only involves one parameter,which is more implementable.We establish the global convergence and sub-linear convergence rate in ergodic sense for the proposed algorithms.The numerical experiments on LASSO regression problems and total variation-based image denoising problem illustrate that our new algorithms outperform the corresponding exact algorithms.
基金This work was supported by the National Natural Science Foundation of China,the Oversea ExchangeFund of Nanjing Normal University,and CNPq of Brazil
文摘In this paper we present some algorithms for minimization of DC function (difference of two convex functions). They are descent methods of the proximal-type which use the convex properties of the two convex functions separately. We also consider an approximate proximal point algorithm. Some properties of the ε-subdifferential and the ε-directional derivative are discussed. The convergence properties of the algorithms are established in both exact and approximate forms. Finally, we give some applications to the concave programming and maximum eigenvalue problems.
基金Coordenação de Aperfeiçoamento de Pessoal de Nível Superior of the Federal University of Rio de Janeiro(UFRJ),Brazil.
文摘In this paper,we present an analysis about the rate of convergence of an inexact proximal point algorithm to solve minimization problems for quasiconvex objective functions on Hadamard manifolds.We prove that under natural assumptions the sequence generated by the algorithm converges linearly or superlinearly to a critical point of the problem.
基金Xing-Ju Cai and Fan Jiang were supported by the National Natural Science Foundation of China(Nos.11871279 and 11571178)Ke Guo was supported by the National Natural Science Foundation of China(Nos.11801455,11871059 and 11971238)+8 种基金China Postdoctoral Science Foundation(Nos.2019M663459 and 2020T130081)the Applied Basic Project of Sichuan Province(No.2020YJ0111)the Fundamental Research Funds of China West Normal University(No.18B031)the Open Project of Key Laboratory(No.CSSXKFKTM202004)School of Mathematical Sciences,Chongqing Normal University.Kai Wang was supported by the National Natural Science Foundation of China(No.11901294)Natural Science Foundation of Jiangsu Province(No.BK20190429)Zhong-Ming Wu was supported by the National Natural Science Foundation of China(No.12001286)the Startup Foundation for Introducing Talent of NUIST(No.2020r003)De-Ren Han was supported by the National Natural Science Foundation of China(Nos.12131004 and 12126603)。
文摘The problem of finding a zero point of a maximal monotone operator plays a central role in modeling many application problems arising from various fields,and the proximal point algorithm(PPA)is among the fundamental algorithms for solving the zero-finding problem.PPA not only provides a very general framework of analyzing convergence and rate of convergence of many algorithms,but also can be very efficient in solving some structured problems.In this paper,we give a survey on the developments of PPA and its variants,including the recent results with linear proximal term,with the nonlinear proximal term,as well as the inexact forms with various approximate criteria.
文摘In this paper we present two inexact proximal point algorithms to solve minimization problems for quasiconvex objective functions on Hadamard manifolds.We prove that under natural assumptions the sequence generated by the algorithms are well defined and converge to critical points of the problem.We also present an application of the method to demand theory in economy.
文摘The proximal point algorithm has many interesting applications,such as signal recovery,signal processing and others.In recent years,the proximal point method has been extended to Riemannian manifolds.The main advantages of these extensions are that nonconvex problems in classic sense may become geodesic convex by introducing an appropriate Riemannian metric,constrained optimization problems may be seen as unconstrained ones.In this paper,we propose an inexact proximal point algorithm for geodesic convex vector function on Hadamard manifolds.Under the assumption that the objective function is coercive,the sequence generated by this algorithm converges to a Pareto critical point.When the objective function is coercive and strictly geodesic convex,the sequence generated by this algorithm converges to a Pareto optimal point.Furthermore,under the weaker growth condition,we prove that the inexact proximal point algorithm has linear/superlinear convergence rate.
文摘We study the existence of best proximity points for single-valued non-self map-pings. Also, we prove a best proximity point theorem for set-valued non-self mappings in metric spaces with an appropriate geometric property. Examples are given to support the usability of our results.
基金The work was supported in part by the Shanghai Youth Science and Technology Talent Sail Plan(No.15YF1403400)the National Natural Science Foundation of China(No.61321064).
文摘A unified efficient algorithm framework of proximal-based decomposition methods has been proposed for monotone variational inequalities in 2012,while only global convergence is proved at the same time.In this paper,we give a unified proof on theO(1/t)iteration complexity,together with the linear convergence rate for this kind of proximal-based decomposition methods.Besides theε-optimal iteration complexity result defined by variational inequality,the non-ergodic relative error of adjacent iteration points is also proved to decrease in the same order.Further,the linear convergence rate of this algorithm framework can be constructed based on some special variational inequality properties,without necessary strong monotone conditions.
文摘We present an extension of the proximal point method with Bregman distances to solve variational inequality problems(VIP)on Hadamard manifolds with null sectional curvature.Under some natural assumptions,as for example,the existence of solutions of the VIP and the monotonicity of the multivalued vector field,we prove that the sequence of the iterates given by the method converges to a solution of the problem.Furthermore,this convergence is linear or superlinear with respect to the Bregman distance.
基金supported by the National Natural Science Foundation of China(No.61373174).
文摘This paper presents two proximal-based pre-correction decomposition methods for convex minimization problems with separable structures.The methods,derived from Chen and Teboulle’s proximal-based decomposition method and He’s parallel splitting augmented Lagrangian method,remain the nice convergence property of the proximal point method and could compute variables in parallel like He’s method under the prediction-correction framework.Convergence results are established without additional assumptions.And the efficiency of the proposed methods is illustrated by some preliminary numerical experiments.
基金the National Science Foundation of China under grants(No.61271014)Specialized Research Fund for the Doctoral Program of Higher Education(20124301110003)the Graduated Students Innovation Fund of Hunan Province(CX2012B238).
文摘In this paper,we propose a fast proximity point algorithm and apply it to total variation(TV)based image restoration.The novel method is derived from the idea of establishing a general proximity point operator framework based on which new first-order schemes for total variation(TV)based image restoration have been proposed.Many current algorithms for TV-based image restoration,such as Chambolle’s projection algorithm,the split Bregman algorithm,the Berm´udez-Moreno algorithm,the Jia-Zhao denoising algorithm,and the fixed point algorithm,can be viewed as special cases of the new first-order schemes.Moreover,the convergence of the new algorithm has been analyzed at length.Finally,we make comparisons with the split Bregman algorithm which is one of the best algorithms for solving TV-based image restoration at present.Numerical experiments illustrate the efficiency of the proposed algorithms.
基金The first author is partially supported by Scientific Research Fund of Sofia University,Contract 88/2014
文摘We generalize Ekeland's Variational Principle for cyclic maps. We present applications of this version of the variational principle for proving of existence and uniqueness of best proximity points for different classes of cyclic maps.
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 the Natural Science Foundation of China(11171221)
文摘In this paper, we focus on the real-time interactions among multiple utility companies and multiple users and formulate real-time pricing(RTP) as a two-stage optimization problem. At the first stage, based on cost function, we propose a continuous supply function bidding mechanism to model the utility companies’ profit maximization problem, by which the analytic expression of electricity price is further derived. At the second stage, considering that individually optimal solution may not be socially optimal, we employ convex optimization with linear constraints to model the price anticipating users’ daily payoff maximum. Substitute the analytic expression of electricity price obtained at the first stage into the optimization problem at the second stage. Using customized proximal point algorithm(C-PPA), the optimization problem at the second stage is solved and electricity price is obtained accordingly. We also prove the existence and uniqueness of the Nash equilibrium in the mentioned twostage optimization and the convergence of C-PPA. In addition, in order to make the algorithm more practical, a statistical approach is used to obtain the function of price only through online information exchange, instead of solving it directly. The proposed approach offers RTP, power production and load scheduling for multiple utility companies and multiple users in smart grid. Statistical approach helps to protect the company’s privacy and avoid the interference of random factors, and C-PPA has an advantage over Lagrangian algorithm because the former need not obtain the objection function of the dual optimization problem by solving an optimization problem with parameters. Simulation results show that the proposed framework can significantly reduce peak time loading and efficiently balance system energy distribution.
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.
文摘Linearly constrained convex optimization has many applications.The first-order optimal condition of the linearly constrained convex optimization is a monotone variational inequality(VI).For solving VI,the proximal point algorithm(PPA)in Euclideannorm is classical but abstract.Hence,the classical PPA only plays an important theoretical role and it is rarely used in the practical scientific computation.In this paper,we give a review on the recently developed customized PPA in Hnorm(H is a positive definite matrix).In the frame of customized PPA,it is easy to construct the contraction-type methods for convex optimization with different linear constraints.In each iteration of the proposed methods,we need only to solve the proximal subproblems which have the closed form solutions or can be efficiently solved up to a high precision.Some novel applications and numerical experiments are reported.Additionally,the original primaldual hybrid gradient method is modified to a convergent algorithm by using a prediction-correction uniform framework.Using the variational inequality approach,the contractive convergence and convergence rate proofs of the framework are more general and quite simple.
文摘In this paper, we prove a strong convergence theorem for resolvents of accretive operators in a Banach space by the viscosity approximation method with a generalized contraction mapping. The proximal point algorithm in a Banach space is also considered. The results extend some very recent theorems of W. Takahashi.
基金the National Natural Science Foundation of China(No.70901018)
文摘Sparse signal recovery is a topic of considerable interest,and the literature in this field is already quite immense.Many problems that arise in sparse signal recovery can be generalized as a convex programming with linear conic constraints.In this paper,we present a new proximal point algorithm(PPA) termed as relaxed-PPA(RPPA) contraction method,for solving this common convex programming.More precisely,we first reformulate the convex programming into an equivalent variational inequality(VI),and then efficiently explore its inner structure.In each step,our method relaxes the VI-subproblem to a tractable one,which can be solved much more efficiently than the original VI.Under mild conditions,the convergence of the proposed method is proved.Experiments with l1 analysis show that RPPA is a computationally efficient algorithm and compares favorably with the recently proposed state-of-the-art algorithms.