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A kind of geometric duality also occurs in optimization theory, but not one that reverses dimensions.
A linear program may be specified by a system of real variables ( the coordinates for a point in Euclidean space R < sup > n </ sup >), a system of linear constraints ( specifying that the point lie in a halfspace ; the intersection of these halfspaces is a convex polytope, the feasible region of the program ), and a linear function ( what to optimize ).
Every linear program has a dual problem with the same optimal solution, but the variables in the dual problem correspond to constraints in the primal problem and vice versa.

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