Interface MinMaxProblem<T>
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public interface MinMaxProblem<T>
A minmax problem is a decision rule used in decision theory, game theory, statistics and philosophy for minimizing the possible loss while maximizing the potential gain. Alternatively, it can be thought of as maximizing the minimum gain (maxmin). Given a family of error functions, parameterized by ω, we try to minimize their maximum.- See Also:
- Wikipedia: Minimax
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Method Summary
All Methods Instance Methods Abstract Methods Modifier and Type Method Description RealScalarFunction
error(T omega)
e(x, ω) is the error function, or the minmax objective, for a given ω.List<T>
getOmega()
Get the list of omegas, the domain.RealVectorFunction
gradient(T omega)
g(x, ω) = ∇|e(x, ω)| is the gradient function of the absolute error, |e(x, ω)|, for a given ω.
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Method Detail
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error
RealScalarFunction error(T omega)
e(x, ω) is the error function, or the minmax objective, for a given ω.- Parameters:
omega
- a parameterization of a real scalar function- Returns:
- the error function for a given ω, e(x, ω)
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gradient
RealVectorFunction gradient(T omega)
g(x, ω) = ∇|e(x, ω)| is the gradient function of the absolute error, |e(x, ω)|, for a given ω.- Parameters:
omega
- a parameterization of a real scalar function- Returns:
- gω(x), the gradient of the absolute error for a given ω
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