Nonlinear programming studies the general subdivision in which the objective function pépite the constraints or both contain nonlinear portion. This may pépite may not Lorsque a convex program. In general, whether the program is convex affects the difficulty of solving it.

In this setting the decision is a function in a high dimensional space and therefore the numerical aspects also are challenging.

In the field of approximation algorithms, algorithms are designed to find near-absolu dénouement to Pornographique problems. The usual decision transcription is then année inadequate definition of the problem since it only specifies approuvable conclusion.

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Conic programming is a general form of convex programming. LP, SOCP and SDP can all be viewed as conic programs with the appropriate type of cone.

This problem might have année answer of, say, 4. A corresponding decision problem would be "is there a path from u to v that uses 10 or fewer edges?" This problem can Supposé que answered with a primaire 'yes' pépite 'no'.

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Robust optimization is, like stochastic programming, an attempt to prise uncertainty in the data underlying the optimization problem. Robust optimization aims to find fin that are valid under all possible realizations of the uncertainties defined by an uncertainty supériorité.

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