List of Titles

A DC programming approach for sensor network localization with uncertainties in anchor positions

- Wu, Changzhi, Li, Chaojie, Long, Qiang

Authors: Wu, Changzhi , Li, Chaojie , Long, Qiang
Date: 2014
Type: Text , Journal article
Relation: Journal of Industrial and Management Optimization Vol. 10, no. 3 (2014), p. 817-826
Full Text: false
Reviewed:
Description: The sensor network localization with uncertainties in anchor positions has been studied in this paper. We formulate this problem as a DC (difference of two convex functions) programming. Then, a DC programming based algorithm has been proposed to solve such a problem. Simulation results obtained by our proposed method are better performance than those obtained by existing ones.

A direct optimization method for low group delay FIR filter design

- Wu, Changzhi, Gao, David, Lay Teo, Kok

Authors: Wu, Changzhi , Gao, David , Lay Teo, Kok
Date: 2013
Type: Text , Journal article
Relation: Signal Processing Vol. 93, no. 7 (2013), p. 1764-1772
Full Text: false
Reviewed:
Description: This paper studies the design of FIR filter with low group delay, where the desired phase response is not being approximated. It is formulated as a constrained optimization problem, which is then solved globally. Numerical experiments show that our design method can produce a filter with smaller group delay than that obtained by the existing convex optimization method used in conjunction with a minimum phase spectral factorization method under the same design criteria. Furthermore, our formulation offers us the flexibility for the trade-off between the group delay and the magnitude response directly. It also allows the feasibility of imposing constraints on the group delay. © 2013 Elsevier B.V.
Description: 2003011019

Quick View

A hybrid method combining genetic algorithm and Hooke-Jeeves method for constrained global optimization

- Long, Qiang, Wu, Changzhi

Authors: Long, Qiang , Wu, Changzhi
Date: 2014
Type: Text , Journal article
Relation: Journal of Industrial and Management Optimization Vol. 10, no. 4 (2014), p. 1279-1296
Full Text:
Reviewed:
Description: A new global optimization method combining genetic algorithm and Hooke-Jeeves method to solve a class of constrained optimization problems is studied in this paper. We first introduce the quadratic penalty function method and the exact penalty function method to transform the original constrained optimization problem with general equality and inequality constraints into a sequence of optimization problems only with box constraints. Then, the combination of genetic algorithm and Hooke-Jeeves method is applied to solve the transformed optimization problems. Since Hooke-Jeeves method is good at local search, our proposed method dramatically improves the accuracy and convergence rate of genetic algorithm. In view of the derivative-free of Hooke-Jeeves method, our method only requires information of objective function value which not only can overcome the computational difficulties caused by the ill-condition of the square penalty function, but also can handle the non-diffierentiability by the exact penalty function. Some well-known test problems are investigated. The numerical results show that our proposed method is eficient and robust.

A quasisecant method for solving a system of nonsmooth equations

- Long, Qiang, Wu, Changzhi

Authors: Long, Qiang , Wu, Changzhi
Date: 2013
Type: Text , Journal article
Relation: Computers and Mathematics with Applications Vol. 66, no. 4 (2013), p. 419-431
Full Text: false
Reviewed:
Description: In this paper, the solution of nonsmooth equations is studied. We first transform the problem into an equivalent nonsmooth optimization problem and then the quasisecant method is introduced to solve it. Some nonsmooth equations that have arisen from bilevel programming problems are solved by our proposed method. The numerical results show the effectiveness and efficiency of our proposed method. Â© 2013 Elsevier Ltd. All rights reserved.
Description: 2003011208

Distributed proximal-gradient method for convex optimization with inequality constraints

- Li, Jueyou, Wu, Changzhi, Wu, Zhiyou, Long, Qiang, Wang, Xiangyu

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 optimum design of uniform FIR filter bank with magnitude constraints

- Wu, Changzhi, Teo, Kok Lay, Rehbock, Volker, Dam, Haihuyen

Authors: Wu, Changzhi , Teo, Kok Lay , Rehbock, Volker , Dam, Haihuyen
Date: 2008
Type: Text , Journal article
Relation: IEEE Transactions on Signal Processing Vol. 56, no. 11 (2008), p. 5478-5486
Full Text: false
Reviewed:
Description: The optimum design of a uniform finite impulse response filter bank can be formulated as a nonlinear semi-infinite optimization problem. However, this optimization problem is nonconvex with infinitely many inequality constraints. In this paper, we propose a new hybrid approach for solving this highly challenging nonlinear, nonconvex semi-infinite optimization problem. In this approach, a gradient-based method is used in conjunction with a filled function method to determine a global minimum of the problem. This new hybrid approach finds an optimal result independent of the initial guess of the solution. The method is applied to some existing examples. The results obtained are superior to those obtained by other existing methods. © 2008 IEEE.

Quick View

Gradient-free method for nonsmooth distributed optimization

- Li, Jueyou, Wu, Changzhi, Wu, Zhiyou, Long, Qiang

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.

Min-max optimal control of linear systems with uncertainty and terminal state constraints

- Wu, Changzhi, Lay Teo, Kok, Wu, Soonyi

Authors: Wu, Changzhi , Lay Teo, Kok , Wu, Soonyi
Date: 2013
Type: Text , Journal article
Relation: Automatica Vol. 49, no. 6 (2013), p. 1809-1815
Full Text: false
Reviewed:
Description: In this paper, a class of min-max optimal control problems with continuous dynamical systems and quadratic terminal constraints is studied. The main contribution is that the original terminal state constraint in which the disturbance is involved is transformed into an equivalent linear matrix inequality without disturbance under certain conditions. Then, the original min-max optimal control problem is solved via solving a sequence of semi-definite programming problems. An example is presented to illustrate the proposed method. © 2013 Elsevier Ltd. All rights reserved.
Description: 2003011022

New two-stage method for nonparametric regression with jump points

- Wu, Changzhi, Liu, C, Teo, Kok Lay, Shao, Q

Authors: Wu, Changzhi , Liu, C , Teo, Kok Lay , Shao, Q
Date: 2008
Type: Text , Book chapter
Relation: Control of the chaos in nonlinear circuits and systems Chapter 4 p. 79-94
Full Text: false
Reviewed:
Description: In this chapter, a two-staged method is presented for nonparametric regression with jump points. After the rough location of all the possible jump points are identified using the existing efficient kernel method, a smoothing spline function is used to approximate each segment of the regression function. A time scaling transformation is derived so as to map the undecided jump points into fixed points. This approximation problem is formulated as an optimization problem which can be solved by many existing techniques. The method is applied to several examples. The results obtained show that the method is highly efficient

On the triality theory for a quartic polynomial optimization problem

- Gao, David, Wu, Changzhi

Authors: Gao, David , Wu, Changzhi
Date: 2012
Type: Text , Journal article
Relation: Journal of Industrial and Management Optimization Vol. 8, no. 1 (2012), p. 229-242
Full Text: false
Reviewed:
Description: This paper presents a detailed proof of the triality theorem for a class of fourth-order polynomial optimization problems. The method is based on linear algebra but it solves an open problem on the double-min duality. Results show that the triality theory holds strongly in the tri-duality form for our problem if the primal problem and its canonical dual have the same dimension; otherwise, both the canonical min-max duality and the double-max duality still hold strongly, but the double-min duality holds weakly in a symmetrical form. Some numerical examples are presented to illustrate that this theory can be used to identify not only the global minimum, but also the local minimum and local maximum.

Showing items 1 - 10 of 10