It is well known that equilibrium problems and their generalizations have been important tools for solving problems arising in the fields of linear or nonlinear programming, variational inequalities, complementary problems, optimization problems, fixed point problems and have been widely applied to physics, structural analysis, management science and economics etc.
It is well known that mixed equilibrium problems and their generalizations have been important tools for solving problems arising in the fields of linear or nonlinear programming, variational inequalities, complementary problems, optimization problems, and fixed point problems, and they have been widely applied to physics, structural analysis, management science, economics, etc.
It is well known that equilibrium problems and their generalizations have been important tools for solving problems arising in the fields of linear or nonlinear programming, variational inequalities, complementary problems, optimization problems, fixed-point problems and have been widely applied to physics, structural analysis, management science and economics etc (see, for example [8, 26, 27]).
It is well known that equilibrium problems and mixed equilibrium problems have been important tools for solving problems arising in the fields of linear or nonlinear programming, complementary problems, optimization problems, variational inequalities, fixed point problems and in certain applications to economics, physics, mechanics and engineering sciences, etc.
To overcome the numerical difficulties of the complementary constraints in optimization, an iteration process, comprising a quadratic programming (QP) and an updating process, is employed as the optimization method.
Complementary DNA-AFLP optimization problems can be addressed by computational (in silico) analysis.
They act at different management levels but their management actions are complementary to improve energy optimization through resource optimization when coordinated.
These managers act on different management levels, but their actions are complementary to improve resource optimization when coordinated, hence reducing the energy consumption of the managed system.
The complementary problem of the optimization of the bitrate for a target quality is addressed in [17, 18], reducing the computational load of Tier-1 too.
