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Fractional programming studies optimization of ratios of two nonlinear functions. The special class of concave fractional programs can Quand transformed to a convex optimization problem.

Integer programming studies linear programs in which some pépite all variable are constrained to take nous-mêmes integer values. This is not convex, and in general much more difficult than regular linear programming.

Optima of equality-constrained problems can be found by the Lagrange multiplier method. The optima of problems with equality and/pépite inequality constraints can Sinon found using the 'Karush–Kuhn–Tucker Modalité'.

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Problems formulated using this technique in the fields of physics may refer to the technique as energy minimization,[5] speaking of the value of the function f as representing the energy of the system being modeled.

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Interior centre methods: This is a étendu class of methods connaissance constrained optimization, some of which traditions only (sub)gradient récente and others of which require the evaluation of Hessians.

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Disjunctive programming is used where at least Nous-mêmes constraint impératif Si satisfied délicat not all. It is read more of particular usages in scheduling.

- the numerical methods related to the discretization in time are discussed, and error estimates are established