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Numerical Algebra, Control and Optimization (NACO) is an international journal devoted to publishing peerrefereed high quality original papers on any nontrivial 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.
The journal adheres to the publication ethics and malpractice policies outlined by COPE. 
TOP 10 Most Read Articles in NACO, November 2014
1 
Jensen's inequality for quasiconvex functions
Volume 2, Number 2, Pages: 279  291, 2012
S. S. Dragomir
and C. E. M. Pearce
Abstract
References
Full Text
Related Articles
Some inequalities of Jensen type and connected results
are given for quasiconvex functions on convex sets in real linear spaces.

2 
Some new bounds for two mappings related
to the HermiteHadamard inequality for convex functions
Volume 2, Number 2, Pages: 271  278, 2012
S. S. Dragomir
and I. Gomm
Abstract
References
Full Text
Related Articles
Some new results concerning two mappings associated to the celebrated
HermiteHadamard integral inequality for convex function with applications
for special means are given.

3 
On the HermiteHadamard inequality for convex functions of
two variables
Volume 4, Number 1, Pages: 1  8, 2013
ShuLin Lyu
Abstract
References
Full Text
Related Articles
Inspired by the results in [S. S. Dragomir and I. Gomm, Num. Alg. Cont. $\&$ Opt., 2 (2012), 271278], we give some new bounds for two mappings related to the HermiteHadamard inequality for convex functions of two variables, and apply them to special functions to get some results for the $p$logarithmic mean. We also apply the HermiteHadamard inequality to matrix functions in this paper.

4 
Some useful inequalities via trace function method in Euclidean Jordan algebras
Volume 4, Number 1, Pages: 39  48, 2013
YuLin Chang
and ChinYu Yang
Abstract
References
Full Text
Related Articles
In this paper, we establish convexity of some functions
associated with symmetric cones, called SC trace functions.
As illustrated in the paper, these functions play a key role in
the development of penalty and barrier function methods for
symmetric cone programs. With trace function method we offer much
simpler proofs to these useful inequalities.

5 
An algorithm for the largest eigenvalue of nonhomogeneous nonnegative polynomials
Volume 4, Number 1, Pages: 75  91, 2013
Nur Fadhilah Ibrahim
Abstract
References
Full Text
Related Articles
In this paper, we propose an iterative method for calculating the
largest eigenvalue of nonhomogeneous nonnegative polynomials. This
method is a generalization of the method in [19]. We also
prove this method is convergent for irreducible nonhomogeneous
nonnegative polynomials.

6 
Grasping force based manipulation for multifingered handarm Robot using neural networks
Volume 4, Number 1, Pages: 59  74, 2013
ChunHsu Ko
and JingKun Chen
Abstract
References
Full Text
Related Articles
Multifingered handarm robots play an important role in dynamic manipulation tasks. They can grasp and move various shaped objects. It is important to plan the motion of the arm and appropriately control the grasping forces for the multifingered handarm robots. In this paper, we perform the grasping force based manipulation of the multifingered handarm robot by using neural networks. The motion parameters are analyzed and planned with the constraint of the multiarms kinematics. The optimal grasping force problem is recast as the secondorder cone program. The semismooth Newton method with the FischerBurmeister function is then used to efficiently solve the secondorder cone program. The neural network manipulation system is obtained via the fitting of the data that are generated from the optimal manipulation simulations. The simulations of optimal grasping manipulation are performed to demonstrate the effectiveness of the proposed approach.

7 
The TolandFenchelLagrange duality of DC programs for composite convex functions
Volume 4, Number 1, Pages: 9  23, 2013
Yuying Zhou
and Gang Li
Abstract
References
Full Text
Related Articles
In this paper, by virtue of the epigraph technique, we construct a new kind of closednesstype constraint
qualification, which is the sufficient and necessary condition to guarantee the
strong duality between a cone constraint composite optimization problem
and its dual problem holds. Under this closednesstype constraint qualification condition, we obtain a formula of subdifferential for composite functions and study a cone constraint composite DC optimization problem in locally convex Hausdorff topological vector spaces.

8 
Adjacent vertex distinguishing edgecolorings and totalcolorings of the Cartesian product of graphs
Volume 4, Number 1, Pages: 49  58, 2013
Shuangliang Tian,
Ping Chen,
Yabin Shao
and Qian Wang
Abstract
References
Full Text
Related Articles
Let $G$ be a simple graph with vertex set $V(G)$ and edge set
$E(G)$. An edgecoloring $\sigma$ of $G$ is called an adjacent
vertex distinguishing edgecoloring of $G$ if $F_{\sigma}(u)\not=
F_{\sigma}(v)$ for any $uv\in E(G)$, where $F_{\sigma}(u)$ denotes
the set of colors of edges incident with $u$. A totalcoloring
$\sigma$ of $G$ is called an adjacent vertex distinguishing
totalcoloring of $G$ if $S_{\sigma}(u)\not= S_{\sigma}(v)$ for any
$uv\in E(G)$, where $S_{\sigma}(u)$ denotes the set of colors of
edges incident with $u$ together with the color assigned to $u$. The
minimum number of colors required for an adjacent vertex
distinguishing edgecoloring (resp. an adjacent vertex
distinguishing totalcoloring) of $G$ is denoted by $\chi_a^{'}(G)$
(resp. $\chi^{''}_{a}(G)$). In this paper, we provide upper bounds
for these parameters of the Cartesian product $G$ □ $H$ of two
graphs $G$ and $H$. We also determine exact value of these
parameters for the Cartesian product of a bipartite graph and a
complete graph or a cycle, the Cartesian product of a complete
graph and a cycle, the Cartesian product of two trees and the
Cartesian product of regular graphs.

9 
Partial $S$goodness for partially sparse signal recovery
Volume 4, Number 1, Pages: 25  38, 2013
Lingchen Kong,
Naihua Xiu
and Guokai Liu
Abstract
References
Full Text
Related Articles
In this paper, we will consider the problem of partially sparse
signal recovery (PSSR), which is the signal
recovery from a certain number of linear measurements when its part is known to be sparse. We establish and characterize
partial $s$goodness for a sensing matrix in PSSR. We show that the partial $s$goodness condition is equivalent to the partial null space property (NSP), and is weaker than partial restricted isometry
property. Moreover, this provides a verifiable approach for partial NSP via partial $s$goodness constants. We also give
exact and stable partially $s$sparse recovery via the partial $l_1$norm minimization under mild assumptions.

10 
Some results on $l^k$eigenvalues of tensor and related spectral radius
Volume 1, Number 3, Pages: 381  388, 2011
Chen Ling
and Liqun Qi
Abstract
References
Full Text
Related Articles
In this paper, we study the $l^k$eigenvalues/vectors of a real
symmetric square tensor. Specially, we investigate some properties on the related $l^k$spectral radius of a real
nonnegative symmetric square tensor.

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