Scaling in optimization problems
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Assume a simple example, with an optimization problem:max
subject to
, with
and
given. I want to solve it with fmincon (because there will be other constraints as well), but with
the derivate with dV/dc will get ever smaller with t.
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A similar problem occurs when we have:
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Is there a way to scale the problem such that the derivates will be of similar size? Otherwise the solution for large t (or smalle alpha) will be less accurate, right?
(One work around would be to use a value function, and solve for each t, but I don't want to do that).
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Matt J
2023 年 1 月 22 日
編集済み: Matt J
2023 年 1 月 22 日
the derivate with dV/dc will get ever smaller with t.
It's not clear that that matters because the values of c are also influenced by the constraints and their derivatives.
Regardless, though, most fmincon algorithms are Newton-like, which means they already normalize dV/dc by second derivatives.
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