Interface MinMaxProblem<T>


  • 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
    • Method Detail

      • 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)
      • 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
      • getOmega

        List<T> getOmega()
        Get the list of omegas, the domain.
        Returns:
        the set of omegas