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In many applications, objective functions, including loss functions as a particular case, are determined by the problem formulation. In other situations, the decision maker’s preference must be elicited and represented by a scalar-valued function (called also utility function) in a form suitable for optimization — the problem that Ragnar Frisch has highlighted in his Nobel Prize lecture. [4]
Multiple rule-based problems are problems containing various conflicting rules and restrictions. [1] Such problems typically have an " optimal " solution, found by striking a balance between the various restrictions, without directly defying any of the aforementioned restrictions.
The image of a function f(x 1, x 2, …, x n) is the set of all values of f when the n-tuple (x 1, x 2, …, x n) runs in the whole domain of f.For a continuous (see below for a definition) real-valued function which has a connected domain, the image is either an interval or a single value.
Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.
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In the above equations, (()) is the exterior penalty function while is the penalty coefficient. When the penalty coefficient is 0, f p = f . In each iteration of the method, we increase the penalty coefficient p {\displaystyle p} (e.g. by a factor of 10), solve the unconstrained problem and use the solution as the initial guess for the next ...
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