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For strictly concave utility functions, the dual problem always gives the same solution as the primal problem.
As the primal problem is a convex optimization problem, there is no gap between the primal and dual problems.
That is, Algorithm 1 for the dual upper bound finds a solution as long as the primal problem is feasible.
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The minimization of the energy of the primal problem as well as the minimization of the energy of the dual problem with respect to a design function lead to the primal and dual material residuals, respectively.
The primal problem can be formulated as follows by incorporating the discriminant function (3): (5) s.t.
We can formulate the primal problem (PP) as follows: (1).
First, we express the Lagrangian of the primal problem (5) as (6).
Note that the CSI uncertainty bounds ε=[ε 1,·,ε K ] T and λ are regarded as the given parameters for both the primal problem (P ε ) and its dual (D ε ).
Thanks to the linearity property of the expectation, the Lagrangian function of the primal problem (5) can be expressed as (6).
thus obtaining a solution to the primal problem (9) accordingly.
Hence, the primal problem in (5) is equivalent with the following optimization problem.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com