Stochastic programming: Difference between revisions

Revision as of 23:36, 12 March 2005

Revision as of 16:28, 29 March 2005

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In fact, it's deeply linked with the computational abilities, which, up to recently, were really poor. As a consequence, one couldn't afford for a full resolution on our problem (even with a super-computer like a [[Cray]]). Imagine handling a physical system, in a initial state. The idea is to perform a [[random]] update of this state. You randomly pick up an element of your system and check towards which point it would evolve given the current state. If the system is in a lower [[energy]] state, then you perform the update, otherwise you perform the update according to an ''a priori'' given probability. And you [[iterate]] this procedure.

The classic example is the [[Monte- Carlo algorithm]] used for a set of [[magnetic particle]]s. The '''current state''' is the knowledge of spin up/spin down for each particle. You pick up one of them, and you check if the change of spin would decrease or increase the global energy.

[[Category:Optimization algorithms]]