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For this reason, a quadratic nonlinear minimization problem is set up to minimize the decomposition error for a given finite order constraint.
The minimization problem is solved by an iterative method.
This minimization problem is convex and not differentiable.
Moreover, the fuel cost minimization problem is solved using IPM.
An algorithm based on dynamic programming to solve this minimization problem is presented.
In the first step, an auxiliary multiobjective minimization problem is solved.
A volume minimization problem is solved by an extended evolutionary topology optimization approach.
The resulting minimization problem is solved using a BFGS quasi-Newton algorithm.
This minimization problem is attacked with different algorithms both local and global in nature.
The rectangle packing area minimization problem is a key sub-problem of floorplanning in VLSI design.
The constrained minimization problem is represented with linear matrix inequalities (LMI) that can be efficiently solved using convex programming techniques.
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