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The optimized truss profile is obtained by solving a parametric shape optimization problem with Fourier series coefficients as design variables.
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In the second step, these controllers are checked for the worst case of parametric uncertainty by solving a multiobjective maximization problem.
According to the approach, wheel/rail forces generated from railhead roughness as well as from the parametric excitation of the moving wheels can be calculated by solving a set of linear algebraic equations.
In this paper, we propose a new Newton-type method for solving the nonlinear complementarity problem (NCP) based on a class of one-parametric NCP-functions, where an approximate Newton direction can be obtained by solving a modified Newton equation in each iteration.
MI-GRAAL fulfills this by solving a weighted bipartite matching, while GHOST by solving a quadratic problem.
For many thermal processes described by partial differential equations, parametric identification of the model can be performed by solving an inverse problem.
The identification is done by solving an optimization problem.
The polynomial time algorithm described in (Hochbaum, 2010b) for NC′ was based on showing that solving NC′ is equivalent to solving a certain parametric s, t -cut problem.
Explicit solutions to constrained linear MPC problems can be computed by solving multi-parametric quadratic programs (mpQP), where the parameters are the components of the state vector.
Both problems can be solved by a parametric shortest path algorithm.
The core idea of a parametric algorithm is scalarization, by which a multiobjective shortest path problem can be tackled by repeatedly solving a single-objective subproblem.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com