Indrepunktmetoderne
Indrepunktmetoderne, also known as interior-point methods, are a class of algorithms used for solving mathematical optimization problems, particularly linear programming and convex quadratic programming. They are a significant alternative to the simplex method for linear programming. The core idea behind interior-point methods is to approach the optimal solution by moving through the interior of the feasible region, rather than along the boundary as the simplex method does.
These methods typically involve iterative steps. In each iteration, the algorithm takes a step towards the
Interior-point methods have proven to be very efficient for large-scale problems. They often exhibit polynomial time