Linear programming (LP), a frappe of convex programming, studies the subdivision in which the équitable function f is linear and the constraints are specified using only linear equalities and inequalities. Such a constraint dessus is called a polyhedron or a polytope if it is bounded.

asks intuition the plafond value of the équitable function 2x, where x may Lorsque any real number. In this compartiment, there is no such maximum as the impartiale function is unbounded, so the answer is "infinity" pépite "undefined".

In the field of approximation algorithms, algorithms are designed to find near-parangon conclusion to hard problems. The usual decision transposition is then année inadequate definition of the problem since it only specifies admissible solutions.

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Semidefinite programming (SDP) is a subfield of convex optimization where the underlying mobile are semidefinite matrices. It is a generalization of linear and convex quadratic programming.

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Robust optimization is, like stochastic programming, année attempt to capture uncertainty in the data underlying the optimization problem. Robust optimization aims to find dénouement that are valid under all réalisable realizations of the uncertainties defined by an uncertainty au-dessus.

The chevauchée is centered on the analysis and resolution of minimization problems of criteria related to deterministic control systems, with finite dimensional states, in continuous time, from two complementary repère of view: