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JIMO is covered in Science Citation Index Expanded, CompuMath Citation Index, Current Contents/Engineering, Computing and Technology ISI Alerting Services.
JIMO is an international journal devoted to publishing peerreviewed, high quality, original papers on the nontrivial interplay between numerical optimization methods and practically significant problems in industry or management so as to achieve superior design, planning and/or operation. Its objective is to promote collaboration between optimization specialists, industrial practitioners and management scientists so that important practical industrial and management problems can be addressed by the use of appropriate, recent advanced optimization techniques.
It is particularly hoped that the study of these practical problems will lead to the discovery of new ideas and the development of novel methodologies in optimization.
JIMO is published by AIMS and sponsored by the Department of Mathematics and Statistics, Curtin University and the Department of Mathematics, Zhejiang University. 
TOP 10 Most Read Articles in JIMO, April 2014
1 
A nonsmooth Newton's method for discretized optimal control problems with state and control constraints
Volume 4, Number 2, Pages: 247  270, 2008
Matthias Gerdts
and Martin Kunkel
Abstract
Full Text
Related Articles
We investigate a nonsmooth Newton's method for the numerical
solution of discretized optimal control problems subject to
pure state constraints and mixed controlstate constraints.
The infinite dimensional problem is discretized by application of a
general onestep method to the differential equation.
By use of the FischerBurmeister function the first order necessary
conditions for the discretized problem are transformed into an equivalent
nonlinear and nonsmooth equation. This nonlinear and nonsmooth
equation is solved by a globally convergent nonsmooth Newton's method.
Numerical examples for the minimum energy problem and the optimal control of
a robot conclude the article.

2 
Spline function smooth support vector machine for classification
Volume 3, Number 3, Pages: 529  542, 2007
Yubo Yuan,
Weiguo Fan
and Dongmei Pu
Abstract
Full Text
Related Articles
Support vector machine (SVM) is a very popular method for binary
data classification in data mining (machine learning). Since the
objective function of the unconstrained SVM model is a nonsmooth
function, a lot of good optimal algorithms can't be used to find
the solution. In order to overcome this model's nonsmooth
property, Lee and Mangasarian proposed smooth support vector
machine (SSVM) in 2001. Later, Yuan et al. proposed the polynomial
smooth support vector machine (PSSVM) in 2005. In this paper, a
threeorder spline function is used to smooth the objective
function and a threeorder spline smooth support vector machine
model (TSSVM) is obtained. By analyzing the performance of the
smooth function, the smooth precision has been improved obviously.
Moreover, BFGS and NewtonArmijo algorithms are used to solve the
TSSVM model. Our experimental results prove that the TSSVM model
has better classification performance than other competitive
baselines.

3 
A new exact penalty function method for continuous inequality
constrained optimization problems
Volume 6, Number 4, Pages: 895  910, 2010
Changjun Yu,
Kok Lay Teo,
Liansheng Zhang
and Yanqin Bai
Abstract
References
Full Text
Related Articles
In this paper, a computational approach based on a new exact penalty
function method is devised for solving a class of continuous
inequality constrained optimization problems. The continuous
inequality constraints are first approximated by smooth function in
integral form. Then, we construct a new exact penalty function,
where the summation of all these approximate smooth functions in
integral form, called the constraint violation, is appended to the
objective function. In this way, we obtain a sequence of approximate
unconstrained optimization problems. It is shown that if the value
of the penalty parameter is sufficiently large, then any local
minimizer of the corresponding unconstrained optimization problem is
a local minimizer of the original problem. For illustration, three
examples are solved using the proposed method. From the solutions
obtained, we observe that the values of their objective functions
are amongst the smallest when compared with those obtained by other
existing methods available in the literature. More importantly, our
method finds solution which satisfies the continuous inequality
constraints.

4 
Supply chain inventory management via a Stackelberg equilibrium
Volume 2, Number 1, Pages: 81  94, 2006
YeongCheng Liou,
Siegfried Schaible
and JenChih Yao
Abstract
Full Text
Related Articles
In this paper we consider onebuyer, oneseller, finite horizon,
multiperiod inventory models in which the economic order quantity
is integrated with the economic production quantity (EOQEPQ in
short). We introduce the Stackelberg equilibrium framework in
which the objective is to maximize the vendor's total benefit
subject to the minimum total cost that the buyer is willing to
incur. Some existence results, optimality conditions and the
optimal replenishment policy under the Stackelberg equilibrium
concept are obtained and a numerical algorithm and examples are
presented to find the optimal replenishment policy in practice.

5 
A smoothing scheme for optimization problems with MaxMin constraints
Volume 3, Number 2, Pages: 209  222, 2007
X. X. Huang,
Xiaoqi Yang
and K. L. Teo
Abstract
Full Text
Related Articles
In this paper, we apply a smoothing approach to a minimization problem with a maxmin constraint (i.e., a minmaxmin problem). More specifically, we first rewrite the minmaxmin problem as an optimization problem with several minconstraints and then approximate each minconstraint function by a smooth function. As a result, the original minmaxmin optimization problem can be solved by solving a sequence of smooth optimization problems. We investigate the relationship between the global optimal value and optimal solutions of the original minmaxmin optimization problem and that of the approximate smooth problem. Under some conditions, we show that the limit points of the firstorder (secondorder) stationary points of the smooth optimization problems are firstorder (secondorder) stationary points of the original minmaxmin optimization problem.

6 
A penalty function algorithm with objective parameters
for nonlinear mathematical programming
Volume 5, Number 3, Pages: 585  601, 2009
Zhiqing Meng,
Qiying Hu
and Chuangyin Dang
Abstract
Full Text
Related Articles
In this paper, we present a penalty function with objective
parameters for inequality constrained optimization problems. We
prove that this type of penalty functions has good properties for
helping to solve inequality constrained optimization problems.
Moreover, based on the penalty function, we develop an algorithm to
solve the inequality constrained optimization problems and prove its
convergence under some conditions. Numerical experiments show that
we can obtain a satisfactorily approximate solution for some
constrained optimization problems as the same as the exact penalty
function.

7 
On second order symmetric duality in
nondifferentiable multiobjective programming
Volume 5, Number 4, Pages: 697  703, 2009
Xinmin Yang
Abstract
Full Text
Related Articles
In this paper, we point out an inconsistency between assumptions and
results on the second order strong and converse duality in a recent
paper of I. Ahmad ( Information Sciences 173 (2005) 2334). We then
provide appropriate modifications to rectify this deficiency.

8 
Global optimization algorithm
for solving bilevel programming problems with quadratic lower levels
Volume 6, Number 1, Pages: 177  196, 2009
Paul B. Hermanns
and Nguyen Van Thoai
Abstract
Full Text
Related Articles
In this article, we propose a method for finding the global
optimum of a class of nonlinear bilevel programming
problems. The main idea of this method is to construct iteratively a
sequence of points
either ending at an optimal solution of the equivalent problem with a
complementarity constraint, or
converging to an optimal solution. The construction of such a
sequence is performed by using a branchandbound scheme, together
with some relaxation techniques, which are successfully applied in
global optimization. Some illustrative examples and results on
computational experiments are reported.

9 
A smoothing approach for
semiinfinite programming with projected Newtontype algorithm
Volume 5, Number 1, Pages: 141  151, 2008
Zhi Guo Feng,
Kok Lay Teo
and Volker Rehbock
Abstract
Full Text
Related Articles
In this paper we apply the projected Newtontype algorithm to solve
semiinfinite programming problems. The infinite constraints are
replaced by an equivalent nonsmooth function which is then
approximated by a smoothing function. The KKT system is formulated
as a nonsmooth equation. We then apply the projected Newtontype
algorithm to solve this equation and show that the accumulation
point satisfies the KKT system. Some numerical results are presented
for illustration.

10 
A filled function method for constrained nonlinear integer programming
Volume 4, Number 2, Pages: 353  362, 2008
Yongjian Yang,
Zhiyou Wu
and Fusheng Bai
Abstract
Full Text
Related Articles
A filled function method is presented in this paper to solve
constrained nonlinear integer programming problems. It is shown that
for a given nonglobal local minimizer, a better local minimizer can
be obtained by local search staring from an improved initial point
which is obtained by locally solving a boxconstrained integer
programming problem. Several illustrative numerical examples are
reported to show the efficiency of the present method.

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