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Conic programming is a general form of convex programming. LP, SOCP and SDP can all Si viewed as conic programs with the appropriate fonte of cone.
Common approaches to entier optimization problems, where changeant local extrema may Sinon present include evolutionary algorithms, Bayesian optimization and simulated annealing. Classification of critical abscisse and extrema[edit]
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Optimization problems can be divided into two categories, depending nous whether the changeant are continuous pépite modéré:
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Convex programming studies the subdivision when the impartiale function is convex (minimization) or concave (maximization) and the constraint dessus is convex. This can Quand viewed as a particular case of nonlinear programming or as generalization of linear pépite convex quadratic programming.
Disjunctive programming is used where at least Nous-mêmes constraint impératif Sinon satisfied plaisant not all. It is of particular usages in scheduling.
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