Is there any way to define a loss function in optimization problems?
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Philipp Glira on 10 Mar 2021
Edited: Walter Roberson on 10 Mar 2021
Is there any way to define a loss function when setting up an optimization problem using the optimization toolbox?
Main purpose: outlier detection.
Examples for loss functions in other optimization libraries:
- Ceres solver: http://ceres-solver.org/nnls_modeling.html?highlight=loss#lossfunction
- scipy.optimize.least_squares: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.least_squares.html
Yes, absolutely. Without a function to optimize, it's not an optimization problem.
Edited: Matt J on 10 Mar 2021
No, there are no outlier rejection utilities in the Optimization Toolbox solvers. The Computer Vision Toolbox, however, does have a RANSAC routine,
You could also try removing outliers with rmoutliers,
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