An approximate ADMM for solving linearly constrained nonsmooth optimization problems with two blocks of variables
- Authors: Bagirov, Adil , Taheri, Sona , Bai, Fusheng , Wu, Zhiyou
- Date: 2019
- Type: Text , Book chapter
- Relation: Nonsmooth Optimization and Its Applications (part of the International Series of Numerical Mathematics book series) Chapter 2 p. 17-44
- Full Text: false
- Reviewed:
- Description: Nonsmooth convex optimization problems with two blocks of variables subject to linear constraints are considered. A new version of the alternating direction method of multipliers is developed for solving these problems. In this method the subproblems are solved approximately. The convergence of the method is studied. New test problems are designed and used to verify the efficiency of the proposed method and to compare it with two versions of the proximal bundle method.
Quadratic smoothing approximation to 1/2-order exact penalty function
- Authors: Bai, Fusheng , Wu, Zhiyou
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at 1st International Conference on Green Circuits and Systems, ICGCS 2010, Shanghai : 21st-23rd June 2010 p. 409-413
- Full Text: false
- Description: In this paper, we propose a quadratic smoothing approximation to the 1/2-order exact penalty function. It is shown that when the penalty parameter of the smoothed penalty problem with the smoothing approximation function being penalty function is sufficiently large, any global minimizer of the smoothed penalty problem is an approximate feasible point of the original optimization problem, and the difference between the original objective function value on a global minimizer of the smoothed penalty problem and the global optimal value of the original problem can be controlled by the smoothing parameter which can be set in advance. Two numerical examples are reported to show the effectiveness of the proposed quadratic smoothing approximation method. © 2010 IEEE.
A novel monotonization transformation for some classes of global optimization problems
- Authors: Bai, Fusheng , Wu, Zhiyou
- Date: 2006
- Type: Text , Journal article
- Relation: Asia-Pacific Journal of Operational Research Vol. 23, no. 3 (Sep 2006), p. 371-392
- Full Text: false
- Reviewed:
- Description: A novel monotonization method is proposed for converting a non-monotone programming problem into a monotone programming problem. An equivalent monotone programming problem with only inequality constraints is obtained via this monotonization method. Then the existing convexification and concavification methods can be used to convert the monotone programming problem into an equivalent better-structured optimization problem.
- Description: C1
- Description: 2003003590
A global optimization method for solving integer systems of equation
- Authors: Bai, Fusheng , Wu, Zhiyou , Yang, Y. J. , Mammadov, Musa
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at 7th International Conference on Optimization: Techniques and Applications, ICOTA7, Kobe International Conference Center, Japan : 12th-15th December 2007
- Full Text: false
- Description: 2003005717
A filled function method for constrained nonlinear equations
- Authors: Bai, Fusheng , Mammadov, Musa , Wu, Zhiyou , Yang, Yongjian
- Date: 2008
- Type: Text , Journal article
- Relation: Pacific Journal of Optimization Vol. 4, no. 1 (Jan 2008), p. 9-18
- Full Text: false
- Reviewed:
- Description: We consider the problem of solving a constrained system of nonlinear equations. After reformulating the system into an equivalent constrained global optimization problems, we construct a filled function based on a special property of the reformulated problem. A filled function method is then proposed to solve the constrained system of nonlinear equations. Some numerical examples are presented to illustrate the usefulness of the present techniques.
- Description: C1
Lower order calmness and exact penalty function
- Authors: Bai, Fusheng , Wu, Zhiyou , Zhu, D.
- Date: 2006
- Type: Text , Journal article
- Relation: Optimization Methods and Software Vol. 21, no. 4 (2006), p. 515-526
- Full Text: false
- Reviewed:
- Description: In this article, we investigate the exact penalty properties of a lower order penalty function under a lower order calmness conditions. It is shown that the local exact penalization of the lower order penalty function with any positive penalty parameter holds under the local lower order calmness condition. A necessary and sufficient condition for global exact penalization of the lower order penalty function is given in terms of global lower order calmness condition. Furthermore, a formula of least global exact penalty parameter for the lower order penalty function is obtained.
- Description: C1
- Description: 2003002855
Global optimization methods of solving optimization problems and systems of nonlinear equations
- Authors: Bai, Fusheng , Wu, Zhiyou
- Date: 2008
- Type: Text , Conference paper
- Relation: 9th Academic Exchange Meeting of the Operations Research Society of China 2008
- Full Text: false
- Reviewed:
Necessary and sufficient conditions for stable conjugate duality
- Authors: Burachik, Regina , Jeyakumar, Vaithilingam , Wu, Zhiyou
- Date: 2006
- Type: Text , Journal article
- Relation: Journal of Nonlinear Analysis Vol. 64, no. 9 (2006), p. 1998-2005
- Full Text:
- Reviewed:
- Description: The conjugate duality, which states that infx∈X φ(x, 0) = maxv∈Y ' −φ∗(0,v), whenever a regularity condition on φ is satisfied, is a key result in convex anal¬ysis and optimization, where φ : X × Y → IR ∪{+∞} is a convex function, X and Y are Banach spaces, Y ' is the continuous dual space of Y and φ∗ is the Fenchel-Moreau conjugate of φ. In this paper, we establish a necessary and sufficient condition for the stable conjugate duality, ∗ ∗ ∈ X' inf {φ(x, 0) + x ∗(x)} = max {−φ ∗(−x ,v)}, ∀x, x∈Xv∈Y ' and obtain a new global dual regularity condition, which is much more general than the popularly known interior-point type conditions, for the conjugate duality. As a consequence we present an epigraph closure condition which is necessary and sufficient for a stable Fenchel-Rockafellar duality theorem. In the case where one of the functions involved in the duality is a polyhedral convex function, we also provide generalized interior-point conditions for the epigraph closure condition. Moreover, we show that a stable Fenchel’s duality for sublinear functions holds whenever a subdifferential sum formula for the functions holds. As applications, we give general sufficient conditions for a minimax theorem, a subdifferential composition formula and for duality results of convex programming problems.
- Description: C1
- Description: 2003003596
Non-convex quadratic minimization problems with quadratic constraints: Global optimality conditions
- Authors: Jeyakumar, Vaithilingam , Rubinov, Alex , Wu, Zhiyou
- Date: 2007
- Type: Text , Journal article
- Relation: Mathematical Programming Vol. 110, no. 3 (2007), p. 521-541
- Full Text: false
- Reviewed:
- Description: In this paper, we first examine how global optimality of non-convex constrained optimization problems is related to Lagrange multiplier conditions. We then establish Lagrange multiplier conditions for global optimality of general quadratic minimization problems with quadratic constraints. We also obtain necessary global optimality conditions, which are different from the Lagrange multiplier conditions for special classes of quadratic optimization problems. These classes include weighted least squares with ellipsoidal constraints, and quadratic minimization with binary constraints. We discuss examples which demonstrate that our optimality conditions can effectively be used for identifying global minimizers of certain multi-extremal non-convex quadratic optimization problems. © Springer-Verlag 2007.
- Description: C1
A dual criterion for maximal monotonicity of composition operators
- Authors: Jeyakumar, Vaithilingam , Wu, Zhiyou
- Date: 2007
- Type: Text , Journal article
- Relation: Set-Valued Analysis Vol. 15, no. 3 (2007), p. 265-273
- Full Text: false
- Reviewed:
- Description: In this paper we present a dual criterion for the maximal monotonicity of the composition operator T:=A* SA, where S:Y→→ Y is a maximal monotone (set-valued) operator and A: X→ Y is a continuous linear map with the adjoint A*, X and Y are reflexive Banach spaces, and the product notation indicates composition. The dual criterion is expressed in terms of the closure condition involving the epigraph of the conjugate of Fitzpatrick function associated with S, and the operator A. As an easy application, a dual criterion for the maximality of the sum of two maximal monotone operators is also given. © 2006 Springer Science+Business Media B.V.
- Description: C1
Generalized Fenchel's conjugation formulas and duality for abstract convex functions
- Authors: Jeyakumar, Vaithilingam , Rubinov, Alex , Wu, Zhiyou
- Date: 2007
- Type: Text , Journal article
- Relation: Journal of Optimization Theory and Applications Vol. 132, no. 3 (Mar 2007), p. 441-458
- Full Text: false
- Reviewed:
- Description: In this paper, we present a generalization of Fenchel's conjugation and derive infimal convolution formulas, duality and subdifferential (and epsilon-subdifferential) sum formulas for abstract convex functions. The class of abstract convex functions covers very broad classes of nonconvex functions. A nonaffine global support function technique and an extended sum- epiconjugate technique of convex functions play a crucial role in deriving the results for abstract convex functions. An additivity condition involving global support sets serves as a constraint qualification for the duality.
- Description: C1
Sufficient global optimality conditions for non-convex quadratic minimization problems with box constraints
- Authors: Jeyakumar, Vaithilingam , Rubinov, Alex , Wu, Zhiyou
- Date: 2006
- Type: Text , Journal article
- Relation: Journal of Global Optimization Vol. 36, no. 3 (2006), p. 471-481
- Full Text: false
- Reviewed:
- Description: In this paper we establish conditions which ensure that a feasible point is a global minimizer of a quadratic minimization problem subject to box constraints or binary constraints. In particular, we show that our conditions provide a complete characterization of global optimality for non-convex weighted least squares minimization problems. We present a new approach which makes use of a global subdifferential. It is formed by a set of functions which are not necessarily linear functions, and it enjoys explicit descriptions for quadratic functions. We also provide numerical examples to illustrate our optimality conditions.
- Description: C1
- Description: 2003001538
Conditions for global optimality of quadratic minimization problems with LMI constraints
- Authors: Jeyakumar, Vaithilingam , Wu, Zhiyou
- Date: 2007
- Type: Text , Journal article
- Relation: Asia-Pacific Journal of Operational Research Vol. 24, no. 2 (2007), p. 149-160
- Full Text: false
- Reviewed:
- Description: In this paper we present sufficient conditions for global optimality of non-convex quadratic programs involving linear matrix inequality (LMI) cnstraints. Our approach makes use of the concept of a quadratic subgradient. We develop optimality conditions for quadratic programs with LMI constraints by using Lagrangian function and by examining conditions which minimizes a quadratic subgradient of the Lagrangian function over simple bounding constraints. As applications, we obtain sufficient optimality condition for quadratic programs with LMI and box constraints by minimizing a quadrtic subgradient over box constraints. We also give optimality conditions for quadratic minimization involving LMI and binary constraints. © World Scientific Publishing Co. & Operational Research Society of Singapore.
- Description: C1
A new local and global optimization method for mixed integer quadratic programming problems
- Authors: Li, G. Q. , Wu, Zhiyou , Quan, Jing
- Date: 2010
- Type: Text , Journal article
- Relation: Applied Mathematics and Computation Vol. 217, no. 6 (2010), p. 2501-2512
- Full Text: false
- Reviewed:
- Description: In this paper, a new local optimization method for mixed integer quadratic programming problems with box constraints is presented by using its necessary global optimality conditions. Then a new global optimization method by combining its sufficient global optimality conditions and an auxiliary function is proposed. Some numerical examples are also presented to show that the proposed optimization methods for mixed integer quadratic programming problems with box constraints are very efficient and stable. Crown Copyright © 2010.
Gradient-free method for nonsmooth distributed optimization
- Authors: Li, Jueyou , Wu, Changzhi , Wu, Zhiyou , Long, Qiang
- Date: 2014
- Type: Text , Journal article
- Relation: Journal of Global Optimization Vol.61, no.2 (March 2014), p.325-340
- Full Text:
- Reviewed:
- Description: In this paper, we consider a distributed nonsmooth optimization problem over a computational multi-agent network. We first extend the (centralized) Nesterov’s random gradient-free algorithm and Gaussian smoothing technique to the distributed case. Then, the convergence of the algorithm is proved. Furthermore, an explicit convergence rate is given in terms of the network size and topology. Our proposed method is free of gradient, which may be preferred by practical engineers. Since only the cost function value is required, our method may suffer a factor up to d (the dimension of the agent) in convergence rate over that of the distributed subgradient-based methods in theory. However, our numerical simulations show that for some nonsmooth problems, our method can even achieve better performance than that of subgradient-based methods, which may be caused by the slow convergence in the presence of subgradient.
A New Objective Penalty Function Approach for Solving Constrained Minimax Problems
- Authors: Li, Jueyou , Wu, Zhiyou , Long, Qiang
- Date: 2014
- Type: Text , Journal article
- Relation: Journal of the Operations Research Society of China Vol. 2, no. 1 (March 2014 2014), p. 93-108
- Full Text: false
- Reviewed:
- Description: In this paper, a new objective penalty function approach is proposed for solving minimax programming problems with equality and inequality constraints. This new objective penalty function combines the objective penalty and constraint penalty. By the new objective penalty function, a constrained minimax problem is converted to minimizations of a sequence of continuously differentiable functions with a simple box constraint. One can thus apply any efficient gradient minimization methods to solve the minimizations with box constraint at each step of the sequence. Some relationships between the original constrained minimax problem and the corresponding minimization problems with box constraint are established. Based on these results, an algorithm for finding a global solution of the constrained minimax problems is proposed by integrating the particular structure of minimax problems and its global convergence is proved under some conditions. Furthermore, an algorithm is developed for finding a local solution of the constrained minimax problems, with its convergence proved under certain conditions. Preliminary results of numerical experiments with well-known test problems show that satisfactorily approximate solutions for some constrained minimax problems can be obtained.
A feedback neural network for solving convex quadratic bi-level programming problems
- Authors: Li, Jueyou , Li, Chaojie , Wu, Zhiyou , Huang, Junjian
- Date: 2014
- Type: Text , Journal article
- Relation: Neural Computing and Applications Vol. 25, no. 3 (2014), p. 603-611
- Full Text: false
- Reviewed:
- Description: In this paper, a feedback neural network model is proposed for solving a class of convex quadratic bi-level programming problems based on the idea of successive approximation. Differing from existing neural network models, the proposed neural network has the least number of state variables and simple structure. Based on Lyapunov theories, we prove that the equilibrium point sequence of the feedback neural network can approximately converge to an optimal solution of the convex quadratic bi-level problem under certain conditions, and the corresponding sequence of the function value approximately converges to the optimal value of the convex quadratic bi-level problem. Simulation experiments on three numerical examples and a portfolio selection problem are provided to show the effi- ciency and performance of the proposed neural network approach.
Distributed proximal-gradient method for convex optimization with inequality constraints
- Authors: Li, Jueyou , Wu, Changzhi , Wu, Zhiyou , Long, Qiang , Wang, Xiangyu
- Date: 2014
- Type: Text , Journal article
- Relation: ANZIAM Journal Vol. 56, no. 2 (2014), p. 160-178
- Full Text: false
- Reviewed:
- Description: We consider a distributed optimization problem over a multi-agent network, in which the sum of several local convex objective functions is minimized subject to global convex inequality constraints. We first transform the constrained optimization problem to an unconstrained one, using the exact penalty function method. Our transformed problem has a smaller number of variables and a simpler structure than the existing distributed primal-dual subgradient methods for constrained distributed optimization problems. Using the special structure of this problem, we then propose a distributed proximal-gradient algorithm over a time-changing connectivity network, and establish a convergence rate depending on the number of iterations, the network topology and the number of agents. Although the transformed problem is nonsmooth by nature, our method can still achieve a convergence rate, O (1/k), after k iterations, which is faster than the rate, O (1/k), of existing distributed subgradient-based methods. Simulation experiments on a distributed state estimation problem illustrate the excellent performance of our proposed method. Copyright © 2014 Australian Mathematical Society.
Global optimality conditions for some classes of polynomial integer programming problems
- Authors: Quan, Jing , Wu, Zhiyou , Li, Guoquan
- Date: 2011
- Type: Text , Journal article
- Relation: Journal of Industrial and Management Optimization Vol. 7, no. 1 (2011), p. 67-78
- Full Text:
- Reviewed:
- Description: In this paper, some verifiable necessary global optimality conditions and sufficient global optimality conditions for some classes of polynomial integer programming problems are established. The relationships between these necessary global optimality conditions and these sufficient global optimality conditions are also discussed. The main theoretical tool for establishing these optimality conditions is abstract convexity.
Sufficient conditions for global optimality of semidefinite optimization
- Authors: Quan, Jing , Wu, Zhiyou , Li, Guoquan , Wu, Ou
- Date: 2012
- Type: Text , Journal article
- Relation: Journal of Inequalities and Applications Vol. 2012, no. 108
- Full Text:
- Reviewed:
- Description: In this article, by using the Lagrangian function, we investigate the sufficient global optimality conditions for a class of semi-definite optimization problems, where the objective function are general nonlinear, the variables are mixed integers subject to linear matrix inequalities (LMIs) constraints as well as bounded constraints. In addition, the sufficient global optimality conditions for general nonlinear programming problems are derived, where the variables satisfy LMIs constraints and box constraints or bivalent constraints. Furthermore, we give the sufficient global optimality conditions for standard semi-definite programming problem, where the objective function is linear, the variables satisfy linear inequalities constraints and box constraints. © 2012 Quan et al.