23 September 2019
The Sequential Convex Programming method (SCP), see Zillober [3, 5, 6], is an extension of the method for moving asymptotes (MMA), see Svanberg . The Sequential Convex Programming method requires the derivatives of all functions present in the topology optimization problem. MMA is a nonlinear programming algorithm that approximates a solution for a topology optimization problem by solving a sequence of convex and separable subproblems. These subproblems can be solved efficiently due to their special structure.
The Sequential Convex Programming method extends MMA to ensure convergence by rejecting steps that do not lead to an optimal solution of the underlying problem. The test for acceptance is done by a merit function and a corresponding line search procedure, see Zillober . The goal of the merit function is to measure the progress and enable the objective function and the constraints to be combined in a suitable way.
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