# postprocessIterate

post-processing of the convex programming soluion.

## Contents

- Syntax
- Description
- Input Arguments
- Output Arguments
- See Also
- References

## Syntax

[sol,costCoeffNext,costSetNext] = postprocessIterate(sol,opts)

## Description

The post-processing of the optimal solution consists of the following steps: - checking feasibility - computation of the volume of the (under-approxmiation) of the candidate terminal region (required for checking convergence) - updating the coefficients of the cost function for the next iteration

## Input Arguments

obj | instance of class computeTermRegNonlinSysOptApproach
sCost | generator scaling factors for obj.costSet (double vector)
slack | vector collecting all slack variables (double vector)
tr_exploit | scalar indicating the exploitation of the trust region (scalar, 0 <= tr_exploit <= 1)
estAbsErr | estimate of the set of abstraction errors (class: interval)

## Output Arguments

obj | updated instance of class computeTermRegNonlinSysOptApproach
auxData | additional solution data (e.g. flag indicating; feasibility) (class: termRegNonlinSysSolutionAuxData)

## See Also

-

## References

[1] L. Schäfer et al. "Scalable Computation of Robust Control Invariant Sets of Nonlinear Systems", in IEEE Transactions on Automatic Control, vol. 69, no. 2, pp. 755-770, 2024

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