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Semi-infinite programming – optimization problem with a finite number of changeant and année infinite number of constraints, pépite année infinite number of variable and a finite number of constraintsPages displaying wikidata effigie as a fallback

The logiciel aims to prepare graduates to deal with the matters that arise in the applied Savoir surrounding optimisation, absolu control and game theory: the mathematical modeling of a problem in terms of minimisation, maximisation or equilibrium, its mathematical analysis (vie, uniqueness, etc.), algorithmic approaches to determine such a problem or tackle it, and the palpable implementation of those methods.

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We shall consider essentially stochastic dynamical systems with modeste time and finite state space, pépite finite Markov chains. This framework already contains the essential difficulties (for instance for long term problems), and allows one to give at the same time an insight of algorithms, mathematical techniques and qualitative properties. We may however consider some examples with infinite state space pépite continuous time.

Semidefinite programming (SDP) is a subfield of convex optimization where the underlying incertain 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 achèvement that are valid under all réalisable realizations of the uncertainties defined by année uncertainty set.

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