How can I ensure that linear constraints are not violated in intermediate iterations when using optimization functions?
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MathWorks Support Team
on 6 Nov 2024 at 0:00
Answered: MathWorks Support Team
on 6 Nov 2024 at 10:37
How can I ensure that linear constraints are not violated in intermediate iterations when using optimization functions?
The documentation page here lists algorithms that guarantee that no bound constraints are violated in intermediate results:
https://www.mathworks.com/help/optim/ug/iterations-can-violate-constraints.html
Which solvers and algorithms are suitable for this?
Accepted Answer
MathWorks Support Team
on 6 Nov 2024 at 0:00
Solvers from the Global Optimization Toolbox like patternsearch or surrogateopt can be better suited for these problems compared to gradient-based solvers like fmincon.
Gradient-based solvers can step out of the feasibility region to get a gradient or gradient estimate.
Furthermore, if the problem characteristics have a high discontinuity on the feasibility bounds, they might also have some simulation effects and "noisiness" the global optimization solvers are more robust against.
If you have to use solvers like fmincon, adapt the objective function to include an early feasibility check and return NaN or Inf if not feasible. These values are treated specially in most solvers and they attempt a different iterative step then.
See:
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