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	Numerical Algebra, Control and Optimization (NACO) is an international journal devoted to publishing peer-refereed high quality original papers on any non-trivial interplay between numerical linear algebra, control and optimization. These three areas are closely related and complementary. The developments of many fundamentally important theories and methods in optimization and control are based on numerical linear algebra. Efficient implementation of algorithms in optimization and control also provides new theoretical challenges in numerical linear algebra. Furthermore, optimization theory and methods are widely used in control theory, especially for solving practical control problems. On the other hand, control problems often initiate new theory, techniques and methods to be developed in optimization. The main objective of NACO is to provide a single forum for and promote collaboration between researchers and practitioners in these areas. Significant practical and theoretical problems in one area can be addressed by the use of appropriate recent advanced theory techniques and methods from the other two areas leading to the discovery of new ideas and the development of novel methodologies in numerical algebra, control and optimization.

TOP 10 Most Read Articles in NACO, June 2012

1	A modified Fletcher-Reeves-Type derivative-free method for symmetric nonlinear equations Volume 1, Number 1, Pages: 71 - 82, 2011 Dong-Hui Li and Xiao-Lin Wang Abstract References Full Text Related Articles In this paper, we propose a descent derivative-free method for solving symmetric nonlinear equations. The method is an extension of the modified Fletcher-Reeves (MFR) method proposed by Zhang, Zhou and Li [25] to symmetric nonlinear equations. It can be applied to solve large-scale symmetric nonlinear equations due to lower storage requirement. An attractive property of the method is that the directions generated by the method are descent for the residual function. By the use of some backtracking line search technique, the generated sequence of function values is decreasing. Under appropriate conditions, we show that the proposed method is globally convergent. The preliminary numerical results show that the method is practically effective.
2	Recent advances in numerical methods for nonlinear equations and nonlinear least squares Volume 1, Number 1, Pages: 15 - 34, 2011 Ya-Xiang Yuan Abstract References Full Text Related Articles Nonlinear equations and nonlinear least squares problems have many applications in physics, chemistry, engineering, biology, economics, finance and many other fields. In this paper, we will review some recent results on numerical methods for these two special problems, particularly on Levenberg-Marquardt type methods, quasi-Newton type methods, and trust region algorithms. Discussions on variable projection methods and subspace methods are also given. Some theoretical results about local convergence results of the Levenberg-Marquardt type methods without non-singularity assumption are presented. A few model algorithms based on line searches and trust regions are also given.
3	Convergence analysis of sparse quasi-Newton updates with positive definite matrix completion for two-dimensional functions Volume 1, Number 1, Pages: 61 - 69, 2011 Yuhong Dai and Nobuo Yamashita Abstract References Full Text Related Articles In this paper, we briefly review the extensions of quasi-Newton methods for large-scale optimization. Specially, based on the idea of maximum determinant positive definite matrix completion, Yamashita (2008) proposed a new sparse quasi-Newton update, called MCQN, for unconstrained optimization problems with sparse Hessian structures. In exchange of the relaxation of the secant equation, the MCQN update avoids solving difficult subproblems and overcomes the ill-conditioning of approximate Hessian matrices. A global convergence analysis is given in this paper for the MCQN update with Broyden's convex family assuming that the objective function is uniformly convex and its dimension is only two. This paper is dedicated to Professor Masao Fukushima on occasion of his 60th birthday.
4	Filter-based genetic algorithm for mixed variable programming Volume 1, Number 1, Pages: 99 - 116, 2011 Abdel-Rahman Hedar and Alaa Fahim Abstract References Full Text Related Articles In this paper, Filter Genetic Algorithm (FGA) method is proposed to find the global optimal of the constrained mixed variable programming problem. The considered problem is reformulated to take the form of optimizing two functions, the objective function and the constraint violation function. Then, the filter set methodology [5] is applied within a genetic algorithm framework to solve the reformulated problem. We use pattern search as local search to improve the obtained solutions. Moreover, the gene matrix criteria [10] has been applied to accelerated the search process and to terminate the algorithm. The proposed method FGA is promising compared with some other methods existing in the literature.
5	A derivative-free trust-region algorithm for unconstrained optimization with controlled error Volume 1, Number 1, Pages: 117 - 145, 2011 Jun Takaki and Nobuo Yamashita Abstract References Full Text Related Articles In this paper, we consider the unconstrained optimization problem under the following conditions: (S1) The objective function is evaluated with a certain bounded error, (S2) the error is controllable, that is, the objective function can be evaluated to any desired accuracy, and (S3) more accurate evaluation requires a greater computation time. This situation arises in many fields such as engineering and financial problems, where objective function values are obtained from considerable numerical calculation or a simulation. Under (S2) and (S3), it seems reasonable to set the accuracy of the evaluation to be low at points far from a solution, and high at points in the neighborhood of a solution. In this paper, we propose a derivative-free trust-region algorithm based on this idea. For this purpose, we consider (i) how to construct a quadratic model function by exploiting pointwise errors and (ii) how to control the accuracy of function evaluations to reduce the total computation time of the algorithm. For (i), we propose a method based on support vector regression. For (ii), we present an updating formula of the accuracy of which is related to the trust-region radius. We present numerical results for several test problems taken from CUTEr and a financial problem of estimating implied volatilities from option prices.
6	A nonconvergent example for the iterative water-filling algorithm Volume 1, Number 1, Pages: 147 - 150, 2011 Simai He, Min Li, Shuzhong Zhang and Zhi-Quan Luo Abstract References Full Text Related Articles Iterative Water-filling Algorithm (IWFA) is a well-known distributed multi-carrier power control method for multi-user communication. It was empirically observed (and conjectured) to be convergent under all channel conditions. In this paper, we present an example showing that IWFA can oscillate, therefore disproving the conjecture.
7	On methods for solving nonlinear semidefinite optimization problems Volume 1, Number 1, Pages: 1 - 14, 2011 Jie Sun Abstract References Full Text Related Articles The nonlinear semidefinite optimization problem arises from applications in system control, structural design, financial management, and other fields. However, much work is yet to be done to effectively solve this problem. We introduce some new theoretical and algorithmic development in this field. In particular, we discuss first and second-order algorithms that appear to be promising, which include the alternating direction method, the augmented Lagrangian method, and the smoothing Newton method. Convergence theorems are presented and preliminary numerical results are reported.
8	CVaR-based formulation and approximation method for stochastic variational inequalities Volume 1, Number 1, Pages: 35 - 48, 2011 Xiaojun Chen and Guihua Lin Abstract References Full Text Related Articles In this paper, we study the stochastic variational inequality problem (SVIP) from a viewpoint of minimization of conditional value-at-risk. We employ the D-gap residual function for VIPs to define a loss function for SVIPs. In order to reduce the risk of high losses in applications of SVIPs, we use the D-gap function and conditional value-at-risk to present a deterministic minimization reformulation for SVIPs. We show that the new reformulation is a convex program under suitable conditions. Furthermore, by using the smoothing techniques and the Monte Carlo methods, we propose a smoothing approximation method for finding a solution of the new reformulation and show that this method is globally convergent with probability one.
9	Improved convergence properties of the Lin-Fukushima-Regularization method for mathematical programs with complementarity constraints Volume 1, Number 1, Pages: 49 - 60, 2011 Tim Hoheisel, Christian Kanzow and Alexandra Schwartz Abstract References Full Text Related Articles We consider a regularization method for the numerical solution of mathematical programs with complementarity constraints (MPCC) introduced by Gui-Hua Lin and Masao Fukushima. Existing convergence results are improved in the sense that the MPCC-LICQ assumption is replaced by the weaker MPCC-MFCQ. Moreover, some preliminary numerical results are presented in order to illustrate the theoretical improvements.
10	Genetic algorithm and Tabu search based methods for molecular 3D-structure prediction Volume 1, Number 1, Pages: 191 - 209, 2011 Abdel-Rahman Hedar, Ahmed Fouad Ali and Taysir Hassan Abdel-Hamid Abstract References Full Text Related Articles The search for the global minimum of a potential energy function is very difficult since the number of local minima grows exponentially with the molecule size. The present work proposes the application of genetic algorithm and tabu search methods, which are called GAMCP (Genetic Algorithm with Matrix Coding Partitioning) [7], and TSVP (Tabu Search with Variable Partitioning) [8], respectively, for minimizing the molecular potential energy function. Computational results for problems with up to 200 degrees of freedom are presented and are favorable compared with other four existing methods from the literature. Numerical results show that the proposed two methods are promising and produce high quality solutions with low computational costs.

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