## Journals

- Advances in Mathematics of Communications
- Big Data & Information Analytics
- Communications on Pure & Applied Analysis
- Discrete & Continuous Dynamical Systems - A
- Discrete & Continuous Dynamical Systems - B
- Discrete & Continuous Dynamical Systems - S
- Evolution Equations & Control Theory
- Foundations of Data Science
- Inverse Problems & Imaging
- Journal of Computational Dynamics
- Journal of Dynamics & Games
- Journal of Geometric Mechanics
- Journal of Industrial & Management Optimization
- Journal of Modern Dynamics
- Kinetic & Related Models
- Mathematical Biosciences & Engineering
- Mathematical Control & Related Fields
- Mathematical Foundations of Computing
- Mathematics in Science and Industry
- Networks & Heterogeneous Media
- Numerical Algebra, Control & Optimization
- AIMS Mathematics
- Conference Publications
- Electronic Research Announcements
- Mathematics in Engineering

### Open Access Journals

JIMO

A trust-region filter-SQP method for mathematical programs with
linear complementarity constraints (MPLCCs) is presented. The method
is similar to that proposed by Liu, Perakis and Sun
[Computational Optimization and Applications, 34, 5-33, 2006]
but it solves the trust-region quadratic programming subproblems at each
iteration and uses the filter technique to promote the global convergence. As a result,
the method here is more robust since it admits the use of Lagrangian
Hessian information and its performance is not affected by any penalty parameter.
The preliminary numerical results on test problems generated by the QPECgen generator
show that the presented method is effective.

NACO

A sequential quadratic programming (SQP) algorithm is presented for solving
nonlinear optimization with overdetermined constraints. In each
iteration, the quadratic programming (QP) subproblem is always
feasible even if the gradients of constraints are always linearly
dependent and the overdetermined constraints may be
inconsistent. The $\ell_2$ exact penalty function is selected as the
merit function. Under suitable assumptions on gradients of
constraints, we prove that the algorithm will terminate at an approximate KKT point
of the problem, or there is a limit point which is either a point
satisfying the overdetermined system of equations or a
stationary point for minimizing the $\ell_2$ norm of the residual of the overdetermined
system of equations.

## Year of publication

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